Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
Authors: Yuankai Luo
Lei Shi
Xiao-Ming Wu
Venue: ICML 2025 poster (2025)
Keywords: Graph Neural Networks
Graph-level Tasks
Graph Transformers
Primary area: deep_learning->graph_neural_networks
TL;DR: We systematically evaluate three classic GNNs against state-of-the-art Graph Transformers (GTs) on 14 well-known graph-level datasets, showing that GNNs match or outperform GTs while maintaining greater efficiency.

Abstract: Message-passing Graph Neural Networks (GNNs) are often criticized for their limited expressiveness, issues like over-smoothing and over-squashing, and challenges in capturing long-range dependencies. Conversely, Graph Transformers (GTs) are regarded as superior due to their employment of global attention mechanisms, which potentially mitigate these challenges. Literature frequently suggests that GTs outperform GNNs in graph-level tasks, especially for graph classification and regression on small molecular graphs. In this study, we explore the untapped potential of GNNs through an enhanced framework, GNN+, which integrates six widely used techniques: edge feature integration, normalization, dropout, residual connections, feed-forward networks, and positional encoding, to effectively tackle graph-level tasks. We conduct a systematic re-evaluation of three classic GNNs—GCN, GIN, and GatedGCN—enhanced by the GNN+ framework across 14 well-known graph-level datasets. Our results reveal that, contrary to prevailing beliefs, these classic GNNs consistently match or surpass the performance of GTs, securing top-three rankings across all datasets and achieving first place in eight. Furthermore, they demonstrate greater efficiency, running several times faster than GTs on many datasets. This highlights the potential of simple GNN architectures, challenging the notion that complex mechanisms in GTs are essential for superior graph-level performance. Our source code is available at https://github.com/LUOyk1999/GNNPlus.

Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
Yuankai Luo
Lei Shi" Xiao-Ming Wu"
Abstract
Graph-level Tasks
900
Message-passing Graph Neural Networks (GNNs) are often criticized for their limited expres- siveness, issues like over-smoothing and over- squashing, and challenges in capturing long-range dependencies. Conversely, Graph Transformers (GTs) are regarded as superior due to their em- ployment of global attention mechanisms, which potentially mitigate these challenges. Literature frequently suggests that GTs outperform GNNs in graph-level tasks, especially for graph classifica- tion and regression on small molecular graphs. In this study, we explore the untapped poten- tial of GNNs through an enhanced framework, GNN which integrates six widely used tech- niques: edge feature integration, normalization, dropout, residual connections, feed-forward net- works, and positional encoding, to effectively tackle graph-level tasks. We conduct systematic re-evaluation of three classic GNNs GCN, GIN, and ennhanee by the GNN+ frame- work across 14 well-known graph-level datasets. Our results reveal that, contrary to prevailing be- liefs, these classic GNNs consistently match or surpass the performance of GT's securing top- three rankings across all datasets and achieving first place in eight. Furthermore, they demon- strate greater efficiency, running several times faster than GT's on many datasets. This high- lights the potential of simple GNN architectures, challenging the notion that complex mechanisms in GTs essential for superior graph-level performance. Our source code is available O hutps://ittua.comm//o00y11999/99
Thaining
Node-level
Training
Validazion
Figure I. Differences between graph-level and node-level tasks.
1. Introduction
Graph machine learning addresses both graph-level tasks and node-level tasks, as illustrated in Figure These tasks fundamentally differ their choice of the basic unit for dataset composition, splitting, and training, with graph-level tasks focusing the entire graph, while node-level tasks focus on individual nodes. Graph-level tasks (Dwivedi et al., 2023; Hu et al., 2020; Luo et al., 2023b;a) often involve the classification of relatively small molecular graphs in chemistry (Duvenaud et al., 2015; Morris et al., 2020) or the prediction of protein properties biology (Dwivedi et al., 2022). In contrast, node-level tasks typically involve large social networks (Tang al., 2009) citation networks (Yang al., 2016), where the primary goal is node classifi- cation. This distinction in the fundamental unit of dataset leads to differences in methodologies, training strategies, and application domains.
busssage-passing Graph Neural Networks (GNNs) (Gilmer et al., 2017), which iteratively aggregate information from local neighborhoods to learn node representations, have be- come the predominant approach for both graph-level and node-level tasks (Niepert et al., 2016; Kipf & Welling, 2017; Veličković et al., 2018; Xu et al., 2018; Bresson & Laurent, 2017; Wu et al., 2020). Despite their widespread success, GNNs exhibit several inherent limitations, including re-
'Beihang University "The Hong Kong Polytechnic University. "Corresponding authors: Lei Shi leishi, luoyk) ) @ buaa.edu.en>>. Xiao-Ming Wu Exiao-ming.ae wu polyu.edu.hk>.
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

---

Can Classic GNNs Be Strong Baselines for Graph-level Tasks?
stricted expressiveness (Xu et al., 201 8; Morris et al., 2019), over-smoothing (Li al., 2018; Chen et al., 2020), over- squashing (Alon & Yahav, 2020), and limited capacity to capture Long-range dependencies (Dwivedi et al., 2022).
GNN+ framework across 14 well-known graph-level
datasets from GNN Benchmark (Dwivedi et al., 2023), LRGB (Dwivedi et al., 2022), and OGB (Hu et al., 2020). The results show that the enhanced versions of classic GNNs match or even outperform state-of-the-art (SOTA) GTss con- sistently securing top-three rankings and achieving first place in eight datasets. Moreover, they exhibit superior efficiency. running several times faster than GTs on many datasets. These findings provide a positive answer the pre- viously posed question, suggesting that the true potential of GNNs for graph-level applications has been underestimated. The GNN+ framework effectively unlocks this potential while addressing inherent limitations.
A prevalent perspective is that Graph Transformers (GTs) (Müller et al., 2023; Min et al., 2022; Luo et al., 2024c;a), as an alternative to GNNs, leverage global attention mech- anisms that enable each node attend to all others (Yun et al., 2019; Dwivedi & Bresson, 2020), effectively model- ing long-range interactions and addressing issues such as over-smoothing, over-squashing. and limited expressiveness (Rampášek et al., 2022; Zhang et al., 2023). Consequently, GTs have become the preferred method for graph-level tasks, especially in the context of small molecular graphs (Ma et al., 2023). However, the quadratic complexity of global attention mechanisms limits the scalability of GTs in large- scale, real-world applications (Behrouz & Hashemi, 2024; Sancak et al., 2024; Ding et al., 2024). Moreover, it has been noted that many state-of-the-art GTs (Chen et al., 2022;
Furthermore, in our ablation study, we highlight the impor- tance of each technique integrated into GNN+ Specifically,
(1) Edge features are particularly beneficial in molecu- lar and image superpixel datasets, where they encode crucial domain-specific information.
Rampášek et al., 2022; Shirzad et al., 2023; Ma et al., 2023) still rely-either explicitly or implicitly-on the message passing mechanism of GNNs learn local node representa- tions, thereby enhancing performance.
(2) Normalization becomes more crucial as the scale of datasets increases.
Dropout benefits most graph-level datasets, where a modest dropout rate proves both sufficient and optimal.
Recent studies (Luo et al., 2024b; 2025a;b) have demon- strated that classic GNNs such as GCN (Kipf& & Welling, 2017), GAT (Veličković et al., 2018), and Graph- SAGE (Hamilton et al., 2017) can achieve performance com- parable to, or even exceeding, that of state-of-the the-the-rtt GTs for nove-level tasks. However, a similar conclusion has not yet been established for graph-level tasks. While Tönshoff et al. (2023) conducted pioneering research demonstrating that tuning few hyperparameters can significantly enhance the performance of classic GNNs, their results indicate that these models still do not match the overall performance of GTs. Furthermore, their investigation limited to the Long- Range Graph Benchmark (LRGB) (Dwivedi et al., 2022). This raises important question: "Can classic GNNs also excel in graph-level tasks?"
(4) Residual connections are consistently essential, except in shallow GNNs applied to small graphs.
(5) FFN is especially pivotal for simpler models, such as GCN, in graph-level tasks.
(6) Positional encoding plays a more critical role in small- scale datasets compared to large-scale ones.
These findings underscore the nuanced roles each technique plays in enhancing the performance of classic GNNs, guid- ing future explorations in GNN design and application.
2. Classic GNNs for Graph-level Tasks
Define a graph as 9 = (V,E, x, E). where V is the set of nodes, and & C V x V is the set of edges. The node feature matrix is X glvixdy, where |V] is the number of nodes, and dy is the dimension of the node features. The edge feature matrix is E E [R(E where [ε] is the number of edges and dg is the dimension of the edge features. Let A E R | denote the adjacency matrix of G.
To thoroughly investigate this question, we introduce GNN+ an enhanced GNN framework that incorporates es- tablished techniques into the pressage-passingg mechanism, to effectively address graph-level tasks. As illustrated in Fig. 2, GNN+ integrates six widely used techniques: the incorporation of edge features (Gilmer et al., 2017), normal- ization (loffe& & Szegedy, 2015), dropout (Srivastava et al., 2014), residual connections (He et al., 2016), feed-forward networks (FFN) ( (Vaswani et al., 2017), and positional en- coding (Vaswani et al., 2017). Each technique serves as a hyperparameter that can be tuned to optimize performance.
Message-passine Graph Neural Networks (GNNs) com- pute node representations h', at each layer I via message- passing mechanism, defined by Gilmer et al. (2017):
h! UPDATE Md-1,AGE
We systematically re-evaluate 3 classic GNNs-GCN (Kipf & Welling, 2017), GIN (Xu al.. 2018), and Gat- edGCN (Bresson & Laurent, enhanced by the
where No)) represents the neighboring nodes adjacent to e, AGG' is the message aggregation function, and UPDATE' is the update function. Initially, each node e is assigned a
2

---

Can Classic GNNs Be Strong Basclines for Graph-level Tasks?
feature vector h F E Rd. The function AGG' is then used to aggregate information from the neighbors of to update its representation. The output of the last layer L, i.e., GN A, X)= = h is the representation of v produced by the GNN. In this work, we focus on three classic GNNs: GCN (Kipf & Welling, 2017), GIN (Xu et al., 2018), and GatedGCN (Bresson Laurent, 2017), which differ their approach learning the node representation h.
Add
Readout
MLP
PEN
Residual Connection
Edge Features
Layer
Activation & Dropout
Graph Convolutional Networks (GCN) (Kipf & Welling, 2017), the vanilla GCN model, is formulated as:
Positional
Enceding
Normalization
h1 = E
h W'),
WEA(E)U(E} V dudd
Features
Message Pussing
where d, 11 EuENN)())) Eue denotes the degree of node v, W is the trainable weight matrix in layer I, and is the activation function, e.g., ReLU(.)
Figure 2. The architecture of GNN+
Graph Isomorphism Networks (GIN) (Xu et al., 2018) learn node representations through a different approach:
representations to compute the readout result, expressed as:
h MLP' c) h-'
Σ
)
(3)
h
rendout (fn1) : : vevi)).
(5)
uEN(e)
where V: represents the set of nodes in the graph Gr. For example, for graph prediction tasks, which aim to make predictions about the entire graph, the readout function R often operates as a global mean pooling function.
where is constant, typicallyset to 0, and MLP denotes multi-layer perceptron, which usually consists of 2 layers.
Residual Gated Graph Convolutional Networks (Gat- edGCN) (Bresson & Laurent, 2017) enhance traditional graph convolutions by incorporating gating mechanisms, improving adaptability and expressiveness:
Finally, for any graph Gú the readout result is passed through prediction head g(-) to obtain the predicted label û; g(h (h (eadout) The training objective is minimize the total Eg,eTmi E(U,, y:) w.r.t all graphs in the training set I train. where y, represents the ground-truth label of G. and O denotes the trainable GNN parameters.
hi = h
1-1 W
+
Σ wEN(e)
no,u O h 'w',
(4)
loss L(
where 70,u o'hht'''' hh - is the gating func- tion, and a denotes the sigmoid activation function. This gating function determines how much each neighboring node contributes to updating the representation the cur- rent node. The matrices W W W W are trainable weight matrices specific the layer
3. GNN+ Enhancing Classic GNNs for Graph-level Tasks
We propose an enhancement to classic GNNs for graph-level tasks by incorporating six popular techniques: edge feature integration, normalization, dropout, residual connections, feed-forward networks (FFN), and positional encoding. The enhanced framework, GNN+ is illustrated in Figure
Graph-level tasks treat the entire graph, rather than indi- vidual nodes edges, as the fundamental unit for dataset composition, splitting, and training. Formally, given la- beled graph dataset = ((G,, y each graph G. is associated with label vector y,, representing either cat- egorical labels for classification or continuous values for regression. Next, the dataset I' typically split into training, validation, and test sets, denoted as r Turin UT U T lesa-
3.1. Edge Feature Integration
Edge features were initially incorporated into some GNN frameworks (Gilmer al., 2017: Hu al., 2019) by directly integrating them into the message-passinng process hance information propagation between nodes. Following this practice, GraphGPS (Rampášek et al., 2022) and subse- quent GTs encode edge features within their local modules to enrich node representations.
Graph-level tasks encompass inductive prediction tasks that operate on entire graphs, as well as on individual nodes or edges (Dwivedi et al., 2022), with each corresponding to a distinct label vector y: Each type of task requires a tai- lored graph readout function R, which aggregates the output
Taking GCN (Eq. 2) as an example, the edge features are
3

---

Can Classic GNNs Be Strong Basclines for Graph-level Tasks?
integrated into the massage-passing process as follows:
Table I. Overview of the datasets used for graph-level tasks.
h = = E 1
Vdnd. h W'' + Cue W.),
(6)
Dataset
graphs
Avg. nodes
Avg. # edggs
Task Type
ZINC
12,000
23.2
24.9
Graph regression
uEN(o)U(u)
MNIST
70,000
70.6
564.5
Graph classilication
where W is the trainable weight matrix in layer I, and Bwv the feature vector of the edge between u and e.
CIFARIO
60,000
117.6
941.1
Graph clussification
PATTERN
14,000
118.9
3,039.3
Inductive node cls.
CLUSTER
12,000
117.2
2,150.9
Inductive node cls.
Peprides-func
15,535
150.9
307.3
Graph classification
Peplides-strac struct
15,535
150.9
307.3
Graph regression
3.2. Normalization
PasealVOC-SP
11,355
479.4
2,710.5
Inductive node cls.
COCO-SP
123,286
476.9
2,693.7
Inductive node cls.
Normalization techniques play a critical role stabilizing the training of GNNs by mitigating the effects of covariate shift, where the distribution of node embeddings changes across layers during training. By normalizing node em- beddings at each layer, the training process becomes more stable, enabling the use of higher learning rates and achiev- ing faster convergence (Cai et al., 2021).
MalNet-Tiny
5,000
1,410.3
2,859,9
Graph classification
agbg molhhiv
41,127
25.5
27.5
Graph classification
ogbg-molpcba.
437,929
26.0
28.1
Graph classification
ogbg ppu
158,100
243.4
2.266.1
Gruph clussification
ogbg-code2
452,741
125.2
124.2
Graph classification
While deeper networks, such as deep CNNs (He et al., 2016; Huang et al., 2017), are capable of extract more complex fea- tures, GNNs encounter challenges like over-smoothing (Li et al., 201 8), where deeper models lead to indistinguishable node representations. Consequently, most GNNs are shal- low, typically with 2 to 5 layers. However, by incorporating residual connections, we show that deeper GNNs, ranging from to 20 layers, can achieve strong performance.
Batch Normalization (BN) (loffe & Szegedy, 2015) and Layer Normalization (LN) (Ba al., 2016) are widely used techniques, typically applied to the output of each layer before the activation function (.). Here, we use BN:
h2 <BN( uEN(ojU(E} Vd
Σ
1
h ww' + Euv W))).
(7)
3.5. Feed-Ford Forrard Network
3.3. Dropout
GTs incorporate a feed-forward network (FFN) as a crucial component within each of their layers. The FFN enhances the model's ability to perform complex feature transforma- tions and introduces non-linearity, thereby increasing the network's s expressive power. Inspired by this, we propose appending a fully-connected FFN at the end of each layer of GNNs, defined as:
Dropout (Srivastava et al., 2014), technique widely used in convolutional neural networks (CNNs) to address overfitting by reducing co-adaptation among hidden neurons (Hinton et al., 2012; Yosinski et al., 2014), has also been found to be effective in addressing similar issues in GNNs (Shu al., 2022), where the co-adaptation effects propagate and accu- mulate via message passing among different nodes. Typi- cally, dropout is applied to the embeddings after activation:
FFN(h) = BN (EN, W WHN, FFN, + hh), (10)
where W FFN, and W FFN, are the trainable weight matrices of the FFN at the I-th GNN layer. The node embeddings output by the FFN are then computed as:
h Dropout(o uEN(O)U(o) Vaudd
Σ
1
h
1-1 'w
+eav te
W)))
(8)
h = FFN (ropoutttt(
uEN())U(() Vildo h 1 'w!
Σ
3.4. Residual Connection
eun Hen W!))) h
Residual connections (He et al., 2016) significantly enhance CNN performance by directly connecting the input of layer to its output, thus alleviating the problem of vanishing gra- dient. They were first adopted by the vanilla GCN (Kipf & Welling. 2017) and has since been incorporated into subse- quent works such as GatedGCN (Bresson & Laurent, 2017) and DeepGCNs (Li et al., 2019). Formally, residual connec- tions can be integrated into GNNs as follows:
(1I)
3.6. Positional Encoding
Positional encoding (PE) was introduced in the Transformer model (Vaswani et al., 2017) to represent the positions of tokens within sequence for language modeling. In GTs, PE is used to incorporate graph positional or structural infor- mation. The encodings are typically added or concatenated to the input node features x. before being fed into the GTs. Various PE methods have been proposed, such as Laplacian Positional Encoding (LapPE) (Dwivedi & Bresson, 2020;
h (ropout(o((B))
Σ
h ''''w'
+ew w!))) h-1
(9)

---

Can Classic GNNs Be Strong Basclines for Graph-Ievel Tasks?
Table 2. Test performance on five henchmarks from (Dwivedi al., 2023) (%). Shown is the mean + s.d. of 5 runs with different random
seeds. + denotes the enhanced version, while the baseline results were obtained from their respective original papers. # Param ~ 500K
for ZINC, PATTERN, and CLUSTER, and 100K for MNIST and CIFARIO. The top 1st, 2nd and 3rd results are highlighted.
ZINC
MNIST
CIFARIO
PATTERN
CLUSTER
graphs
12,000
70,000
60,000
14,000
12,000
Avg. @ nodes
23.2
70.6
117.6
118.9
117.2
Avg. # edges
24.9
564.5
941.1
3039.3
2150,9
Metric
MAE.
Accuracy t
Accuracy f
Accuracy f
Accuracy f
GT (2020)
0.226 =: 0.04
90.831 0.6
59.753 +: 0.203
84,808 :: 0.06
73.169 d: 0.822
SAN (2021)
0.139 0.005
86.581 1: 0.807
76.691 1: 0.80
Graphormer (2021)
0.122 0.005
SAT (2022)
0.094 0.005
86.848 0.087
77.856 0.104
EGT (2022)
0.108 = 6009
98.173 = 0:07
68.702 2: 0.400
86.821 + 0.000
79.232 1: 0.548
GraphGPS (2022)
0.070
98.051 0.126
72:298 0.356
86.685
78.016 0.150
GRPE (2022)
0.094
87.020 0.042
Staphormer-URPP! (2022)
0.086 0.007
Graphormer-Gr (2023)
0.081 2: 00009
Specformer (2023)
0.066
LGI-GiT (2023)
86,930 0.040
GPTrans-Ne (2023bb)
36.731 1: 0.085
Graph VIT/MLP-- (2023)
0.073 3073:000000 0:001
98.460 0.00
73,960 :: 0.590
Exphormer (2023)
98.414 0.03
74.754 1: 0.194
86..74 1: 0.003
GRIT (2023)
0.059
98. 108
16.468 0180
87.196
80,026
GRED (2024)
0.077 2: 0.002
98.383 0.02
76.853 :: 0.185
86,759 :: 0.000
78.495 :: 0.103
GRAFT (2024)
98.513 1: 0.86
76.634 :: 0.427
36.993- 1: 0.006
TIGT (2024)
0.057 0002
98.231 0.132
73.963 à: 0.501
86.681 +: 0.002
78.025 :: 0.223
Cluster-GT (2024a)
0.071 0004
GMN (2024)
98.391 0.001
74.560 0.381
87.090 1.209
Gruph-Mamba (2024)
98.420 d: 0,000
73.700 d: 0,549
86,710 d: 0.000
76.800 d: 0.300
GCN
0.367 + 0.013
90.705 0218
55.710 + 0.381
71.892 + 0.334
68.498 + 0.996
GCN
0.076 + 0.009 79.3%
98.382 n.ms 8,5 %
69.824 + 0.413 25.4 %
87.02 + 0,005 21.1 %
77.109 0.872 12.6%
GIN
0.526 0081
96.485
0.89
55.255 1.500
85.387 :: 0.196
64.716 1.583
GIN
0.065 nmm 87.6% %
98.285 0.00 1.9% T
69.592 a. 0.287 25.9 %
86,842 0.033 1.7 %
74.794 a: 0.113 15.6%
GatedGCN
0.282 u.15
97.340 0.43
67.312 a: 0311
85.568
0.064
73.840
0.336
GatedGCN
0.077 0005 72.7%
98,712
0.131 1.4%*
77.218 :: 0.581 14.7% f
87.029 0.007 1.7%
79.128 0.238 7.1%
Time (epoch) of GraphGPS
21s
76s
64s
32s
86s
Time (epoch) of GCN*
7s
60s
40s
19%
29s
Kreuzer et al., 2021), Weisfeiler-Lehmar Positional Encod- ing (WLPE) (Zhang et al., 2020), Random Walk Structural Encoding (RWSE) (Li al., 2020; Dwivedi et al., 2021; Rampášek et al., 2022), Learnable Structural and Positional Encodings (LSPE) (Dwivedi et al., 2021), and Relative Ran- dom Walk Probabilities (RRWP) (Ma et al., 2023). Follow- ing the practice, we use RWSE, one of the most efficient PE methods, to improve the performance of GNNs as follows:
molpeba, ogbg-ppa, and ogbg-code2 from Open Graph Benchmark (OGB) (Hu et al., 2020). We follow their re- spective standard evaluation protocols including the splits and metrics. For further details, refer to the Appendix A.2.
Baselines. Our main focus lies on classic GNNs: GCN (Kipf & Welling. 2017), GIN (Xu et al., 2018; Hu et al., 2019), GatedGCN (Bresson & Laurent, 2017), the SOTA GTs: GT (2020), GraphTrans (2021), SAN (2021), Graphormer (2021), SAT (2022). EGT (2022),
F [x,||=,
RWSE W PE:
(12)
GraphGPS (2022; 2023), GRPE (2022), Graphormer-URPE (2022), Graphormer-GD (2023), Specformer (2023), LGI-
where HII-] denotes concatenation, x & RWSE of node v, and WP is the trainable weight matrix.
RWSF represents the
GT (2023), GPTrans-Nano (2023b), Graph ViT/MIP--Mxer (2023), NAGphormer (2023a), DIFFormer (2023), MGT
(2023), DRew (2023), Exphormer (2023). GRIT (2023),
4. Assessment: Experimental Setup
GRED (2024), GEAET (2024), Subgraphormer (2024). TIGT (2024), GECO (2024), GPNN (2024), Cluster-GT (2024a), and the SOTA graph state space models (GSSMs): GMN (2024), Graph- Maaph-Mamba (2024), GSSC (2024b). Fur- thermore, various other GTs exist in related surveys (Hoang et al., 2024; Shehzad et al., 2024; Müller et al., 2023), empir- ically shown to be inferior to the GT's we compared against for graph-level tasks. We report the performance results of
Datasets, Table 1. We use widely adopted graph-level datasets in experiments, including ZINC, MNIST, CIFAR1O, PATTERN, and CLUSTER from the GNN Benchmark (Dwivedi et al., 2023); Peptides-func, Peptides- struct, PaSSalVOC-SP. COCO-SP, and MalNet-Tiny Graph Benchmark (LRGB) (Dwivedi et al., 2022; Freitas & Dong, 2021); and ogbg-molhiv, ogbg-
from
5

---

Can Classic GNNs Be Strong Basclines for Graph-Ievel Tasks?
Table Test performance on five datasets from Long-Range Range Graph Benchmarks (LRGB) (Dwivedi al., 2022; Freitas & Dong, 2021).
+ denotes the enhanced version, while the baseline results were obtained from their respective original papers. # Param ~ 500K for all.
Peptides-fune
Peptides-struct
POS-S
COCO-SP
MalNet-Tiny
graphs
15,535
15,535
11,355
123,286
5,000
Avg. nodes
150.9
150,9
479,4
476.9
1,410.3
Avg. odges
307.3
307,3
2.710.5
2,693.7
2,859.9
Metric
Avg. Precision f
MAE 4
Fl score
Fl score
Accuracy
GT (2020)
0.6326 no0M
0.2529 1 00006
0.2694 1 n00s
0,2618 1. 000M
SAN (2021)
0.6439 00005
0.2545 : 0.2500 = 0.0015
0.3230 0.009
0,2592 00158
GraphGPS (2022)
0.6535= 00041
0.3748 0.000
0,3412 + 0,004
0 0.04
GraphGPS (2023)
0.6534 0:001
0.2509 2: 0.0011
0,,440 0.005
0,3884 0
0.9350
NAGphormer (2023a)
0,4006
0,3458 0000
DIFFormer (2023)
0.3988 0.0045
0,3620 0.0012
MGT (2023)
0.6817 = 0.000
0.2455 = 0,0055
DRew (2023)
0.7150
0.2536 1
0.3314 0.000
Graph VIT/MLP-Mixer (2023)
0.6970 0,0000
0,2449 + 0.0016
Exphormer (2023)
0.6258 0.0022
0,2512 = 0,0055
0,3446 = 0.0064
0,3430 = 0.0108
:: 0.9402
GRIT (2023)
0.6988
0.2460
Subgruphormer (2024)
0.6415 00002
0.2475 +: 0.0007
GRED (2024)
0,7133 0.0011
0.2455 1 0,0013
GEAET (2024)
0.6485 0,0055
0.2547 0,000)
0.3933 0.0027
0 0.0022
TIGT (2024)
0.6679 0.0044
0,2485 =: 0,0015
GECO (2024)
0.6975 orces
0.2464 1: 0.0009
0.4210 1 000000
0.3320 1: 0.0022
GPNN (2024)
0.6955 00087
0.2454 ::
Graph-Mumba (2024)
0.6739 0.0077
0.2478 = 0.0016
0.4191 = 0.0126
0.3960 e 0.0175
0,9340 + 0.027
GSSC (2024b)
0.7081 0/000
0.2459 =: 0,0000
0.4561 0,0009
0.9406 1: 0.1644
GCN
0.6860 00060)
0.2460 1 0.5007
0.2078 1 0.001
0.1338
000077
018100.100m
GCN
0.7261
5.9%
0.2421
0.0016 1.6%]
0.3357
0.0007 62.0%
0,2733
0.0411 104.9%
0,9354 + 0.0055 15.5%
GIN
0.6621 0.0067
0,2473 + 0.0017
0.2718
0,2125
0,0001
0.8898 + 0.0055
GIN
0.7059
6.6%*
0.2429
18%
0.3189
0.0005 17.3% f
0,2483
0,0046
16.9% 1
0,9325 0.00300 4.8%
GatedGCN
0.6765
0.2477
1: 0,0009
0,3880
0,0000
0,2922
00008
0,9223 0.00s
GatedGCCN
0.7006
000m 3.6
0,2431
(LBGC) 1.9%
0.4263
a.eer 9.9% f
0.3802
0.8015
30.1 1
0,9460 1 0u857 2.6%
Time (epoch) of GraphGPS
6s
6s
17s
213s
46s
Time (epech) of GCN'
6s
6s
12s
162s
6s
baselines primarily from (Rampášek al., 2022; Tönshoff et al., 2023; Ma et al., 2023; Wang et al., 2024), with the remaining obtained from their respective original papers or official leaderboards whenever possible, as those results are obtained by well-tuned models.
5. Assessment: Results and Findings
5.1. Overall Performance
We evaluate the performance of the enhanced versions of classic GNNs across 14 well-known graph-level datasets.
Hyperparameter Configurations. We conduct hyperpa- rameter tuning on 3 classic GNNs, consistent with the hy- perparameter search space of GraphGPS (Rampášek et al., 2022; Tönshoff et al., 2023). Specifically, we utilize the AdamW optimizer (I (Loshchilov, 2017) with a learning rate from {0.0001, 0.0005, 0.001} and an epoch limit of 2000. As discussed in Section 3, we focus on whether to use the edge feature module, normalization (BN), residual connections, FFN, PE (RWSH), and dropout rates from {0.05, 0.15, 0.2, 0.33, the number of layers from 3 to 20. Considering the large number of hyperparameters and datasets, we do not perform an exhaustive search. Addition- ally, we retrain baseline GTss using the same hyperparam- eter search space and training environments as the classic GNNs. Since the retrained results did not surpass those in their original papers, we present the results from those sources. GNN+ denotes the enhanced version. We report mean scores and standard deviations after 5 independent runs with different random seeds. Detailed hyperparameters are provided in Appendix A.
The enhanced versions of classic GNNs achieved state- of-the-art performance, ranking in the top three across 14 datasets, including first place in 8 of them, while also demonstrating superior efficiency. This suggests that the GNN+ framework effectively harnesses the po- tential of classic GNNs for graph-level tasks and sue- cessfully mitigates their inherent limitations.
GNN Benchmark, Table 2. We observe that our GNN+ implementation substantially enhances the performance of classic GNNs, with the most significant improvements on ZINC, PATTERN, and CLUSTER. On MNIST and CIFAR, GatedGCN+ outperforms SOTA models such as GEAET and GRED, securing top rankings.
Long- MangRRane Graph Benchmark (LRGB), Table 3. The results reveal that classic GNNs can achieve strong perfor- mance across LRGB datasets. Specifically, GCN+ excels on the Peptides func and Peptides strucct datasets. On the other hand, GatedGCN- achieves the highest accuracy on
6

---

Can Classic GNNs Be Strong Basclines for Graph-level Tasks?
Table 4. Test performance in four benchmarks from Open Graph Benchmark (OGB) (Hu et al., 2020). + denotes the enhanced version.
while the baseline results were obtained from their respective original papers. indicates the use of additional pretraining datasets, included here for reference only and excluded from ranking.
ugbg-molhiv
ogbg-molpcha
ogbg-ppa 158, 100
oghg-code2
# graphs
41,127
437,929
452,741
Avg. nodes
25.5
26.0
243.4
125.2
Aug. edges
27.5
28.1
2,266.1
124.2 Fl score
Metric
AUROC
Avg. Precision
Accuracy
GT (2020)
0.6454-0081
0.1670 i 00015 0,1830 0o00r
GraphTrans (2021)
0.2761
SAN (2021)
0.7785 0,2400
0.2765 0.0042
Graphormer (pre-trained) (2021)
0.8051 +: 0.005s
SAT (2022)
0.7522 0006
0.1937 1 0.00x
EGT (pre-trained) (2022)
0,8060 0.0065
0,2961 0.0044
GraphGPS (2022)
0,7880 +: 0.0101
0.2907 +: 0.0028
0.8015 0.0083
0.1894 +: 0.0004
Speeformer (2023)
0.7889 +: 0.0134
0.2972 :: 0.0023
Graph V-T/ML.-MMieer (2023)
0.7997 1: 0,0000
Exphormer (2023)
0.7834 +: 0,0044
0,2849 + 0.0025
GRIT (2023)
0.7835 0.0054
0,2362 0.0020
Subgraphormer (2024)
(2024)
0.80338 1: 8.0192
GECO (2024)
0.7980 4: 0,0000
0.2961 d: 000000
0.7982 = 00042
0.1915 1 0.0000
GSSC (2024b)
0.8035 +: 8.0143
GCN
0.7606 + 0.0097
0,2020 + 0.0024
0.6839 0.0044
0.1507 + 0.0015
GCN
0.801 2 à: 0.0124 5.4%
0,2721
+: nu000 34.7 %
0,8077 (10041 18.1%
0.1787 1 0.000 18.6%
GIN
0.7835 0,0125
0.2266
0.0088
0.6892
0.0100
0. 1.14 + 0,0033
GIN+
0,7928 0.0N9 1.2% % f
0.2703
00024 19.3%
0,8107 (UI8S 17.7%
081803 1: 0.0099 20.6
GaledGCN
0.7687 9,0366
0.2670
00020
0.7531
01003
0,1606 +
GatedGCN
0,8040 8.0164 4.6%
0,2981
0.024 1
0.8258
ruNes 9
0.1896
0.8024 18.1% %
Time (epoch/s) of GmahGGP
96s
196s
276s
1919s
Time (epuch/s) of GCN
16s
91s
178s
476s
Table 5. Ablation study on GNN Benchmark (Dwivedi et al., 2023) (%). indicates that the corresponding hyperparameter is not used in GNN it empirically leads to inferior performance.
Open Graph Benchmark (OGB), Table 4. Finally, we test our method on four OGB datasets. As shown in Table 4, GatedGCN+ consistently ranks among the top three mod- els and achieves top performance on three out of the four datasets. On ogbg-ppa, GatedGCN+ shows an improve- ment of approximately 9%, ranking first on the OGB leader- board. On ogbg-molhiv and ogbg-molpe molppba. GatedGCN+ even matches the performance of Graphormer and EGT pre-trained other datasets. Additionally, on ogbg-code2, GatedGCN+ secures the third-highest performance, under- scoring the potential of GNNs for large-scale OGB datasets.
ZINC
MNIST
CIFARIO
PATTERN
CLUSTER
Metric
MAE L
Accuracy
Accuracy 1
Accuracy 1 Accuracy
GCN+
0.076
98.382 noes 69.824 1a0 87.021 s.0 77.109 4975
(-) Edge.
0.135
98.153
68.256
86.854
(-) Norm
0.107
97. 88.8 mes
60.765 ass
52, $2,769=
16.563 ums
(-) Dropout
97.897= om
65.693
86.764
74.926
()) RC
0.159
95.99 929 new 58, 186
1:30
86,059
16.508 2010
(-) FFN
97.174 ores
63.573
86.746
148
72.606 2 130
(-)
0.127
85.597
75.568
GIN
0.065
98.285
o.1 69,592
86.842
74.794
(-) Edge.
a.e 97.655
= mnss 68. 196 1m0
86.7
new
65. 33.895 5425
(-) Norm
0.096
97.695
ooes 64.918 2:05
86.815
72.119 0:39
(-) Dropout
98.214
66.638
86.836
73.316
5.2. Ablation Study
(-) RC
0.137 sm0
97.675
ams
64.910
86.645
16,800
(-)
0.104 130
11.350
owes
60.582
58.511
To examine the unique contributions of different technique used in GNN+ we conduct a series of ablation analysis by selectively removing elements such as edge feature module (Edge.), normalization (Norm), dropout, residual connec- tions (RC), FFN, PE from GCN+ GIN+, and GatedGCN 1es 16.670= os0
62.175 2215
(-) PE
0,123
86,592
73:925
GatedGCN
0.077 tow 98.712
our 77.218
87.029
cast
79.128 0.25
(-) Edge.
0.119=:am 98.085 noes 72.128
86.879
Ear7 76.075: 6545
(-) Norm
0,088 :30 98.275
osss 71.995
86.942
78.495
(-) Dropout
0.089=
98.225
: mse 70.383
86.802 : 184
77.597 un2
(-) RC
1.106=:am 98.442
no07 75.149
2:155
86.845
(-) FFN
0,098
98.438
76.243
86.935
78.975
The effect of these ablations is assessed across GNN Bench- mark (see Table 5), LRGB, and OGB (see Table 6) datasets.
(-) PI
0.174 1:4=000 taw
cas 85.595
77.515
0285
Our ablation study demonstrates that each module incor- porated in GNN+ GNN+-including edge feature integration, normalization, dropout, residual connections, FFN, and PE--i indispensable; the removal of any single com- ponent results in a degradation of overall performance.
MalNet-Tiny. Furthermore, on PascalVOC-SP and COCO- SP. GatedGCN+ significantly improves performance, se- curing the third-best model ranking overall. These results highlight the potential of classic GNNs in capturing long- range interactions in graph-level tasks.
7

---

Can Classic GNNs Be Strong Basclines for Graph-level Tasks?
Table 6. Ablation study on LRGB and OGB datasets. indicates that the corresponding hyperparameter is not used in GNN as it
empirically leads to inferior performance.
Peptides-fume fune Avg. Precision
Peptides-struct
PaScalVOC-SP Fl score
COCO-SP
MaiNet-TTiny
ogbg-mollii AUROC f
oghg-molpchu
ogbg-ppa Accuracy f
ogbg-code2 FI score f
Metric
MAE 4
FI score f
Accuracy 1
Avg. Precision 1
GCN+
0.7261 00007
0,2421 0000
0.3357 + wooss
0.2733 0.04
0,9354 + 0004
0.8012 + c0024
0,2721 00340
0.8077
0.1787 = 0.02s
(-) Edge.
0.7191 = 00036
0.2942 a: nomes
0.2219
0.0000
0,9292
0003
0.7714 =: e.te
0.2628
0001
0.2994 000000
0.1785 =: 0.003
(-) Norm
0.7107 0.0227
0,2509 = 00026
0.1802 00111
0.2332
2.00ss
0,9236
0.054
0.7753 = 60000
0,2528
0006
0.6705
0.000
0.1679 = 000077
(-) Dropout
0.6748 0000s
0.2549 000e
0.3072 1 tuamy
0.2601
4.000
0.7431
1. oxnas
0.2405
0.7893
cames
0.1641 1. 0.00
(-) RC
0.2734
0.1948
0.000s
0,8916
0.004
0.7520
conss
0.1785 = 0.009
(-) FFN
0,2786 DUMMS
0.2314
= 0.0003
0
0.7432 + 6X002
0.2621
000099
caom 0.7672
0.1594 0.0000
(-) PE
0,7069
0.2447
0.7593 =: 60061
0.2667
GIN
0.7059 000004
0.2429 0000
0.3189
1. hanns
0.2483
0,9325
0.000
0.7928 1. |.000
0.2703
0.000
0.8107
1.0000
0.1803 0.000
(-) Edge.
0,7033 10003
0.2442 wozzs
0,2956
0.2259
0.0063
0,9286
0.7597
+: tutus
0.2702 100221
0.2789
0.1752 =: 0.000
(-) Norm
0.6934 w00m
0.2444
w0015
0.2707
tuxe77
0.2244
:: 0.0003
0,9322
wazzs
0.7874 1: 0ut114
0.2556
0.6484
a: cuple
0.1722 :: 0.0044
(-) Dropout
0.6384
0.2531
000300
0.3153 00113
0.2545
0.7673
0.000
0.1730 0.0018
(-) RC
0.6975
0.2527
0.2350
0.1741
0,9150
0.7733
1 0007
0.1454
0.1617
(-) FEN
0.2393
0.1599
0.8944
0003
0.2534
0.6676
0.1491
(-) PE
0.6855
0,2455
0.3141
0.7791
1: DU120
0.2601
CatedGCN+
0.7006
0.2431
0.4263
00057
0.3802
0.0015
0.9460
0.0077
0,8040
00064
0,2981
0.8258 c.00s
0.1896 + 0.004
(-) Edge.
0.6882
0.2466
0.3764
0.3172
0.9372
0.7831 noss>
0.2951
0.0948 0/00000
0.1891
(-) Norm
0,6733
0.2474
0.3628
0.3527
0,9326
0.7879
0.2748
0.6864 cane
0.1743 0.00
(-) Dropout
0.6695
0,2508
0.3389
0.3393
0.2582
0.8088
0.1724
(-) RC
0,2498
00034
0.4075
0u0022
0.3475
0.006
0,9402
0.054
0.7833
60117
0.2897
0,8099
cames
0.1844
0.0055
(-) FHN
0.3508
0.9364
0.2875
00097
0.1718 a 0004
(-) PB
0.6729 00038
0.2461 00925
0,4052 00031
0.7771 6:067
0.2813 00002
Observation 1:
The integration of edge features is par- ticularly effective in molecular and image superpixel datasets, where these features carry critical information.
embeddings is essential for stabilizing the training process.
Observation 3: Dropout proves advantageous for most datasets, with a very low dropout rate being sufficient and optimal.
In molecular graphs such as ZINC and ogbg-molhiv, edge features represent chemical bond information, which is es- sential for molecular properties. Removing this module leads to a significant performance drop. In protein networks ogbg-ppa, edges represent normalized associations between proteins. Removing the edge feature module results in sub- stantial accuracy decline, ranging from 0.5083 to 0.7310 for classic GNNs. Similarly, in image superpixel datasets like CIFAR-10, PascalVOC-SP, and COCO-SP, edge features encode spatial relationships between superpixels, which are crucial for maintaining image coherence. However, in code graphs such as ogbg-code2 and MalNet-Tiny, where edges represent call types, edge features are less relevant to the prediction tasks, and their removal has minimal impact.
Our analysis highlights the crucial role of dropout in main- taining the performance of classic GNNs on GNN Bench- mark and LRGB and large-scale OGB datasets, with its ablation causing significant declines- forr instance, an 8.8% relative decrease for GatedGCN+ on CIFAR- 10 and a 20.4% relative decrease on PascalVOC-SP. This trend continues in large-scale OGB datasets, where removing dropout results in a 5-13% performance drop across 3 classic GNNs on goog-molpeba. Notably, 97% of the optimal dropout rates are < 0.2, and 64% are S 0.1, indicating that very low dropout rate is both sufficient and optimal for graph-level tasks. Interestingly, this finding for graph-level tasks con- trasts with Luo et al. (2024b)'s 's observations for node-level tasks, where a higher dropout rate is typically required.
Observation 2: Normalization tends to have a greater impact on larger-scale datasets, whereas its impact is less significant on smaller datasets.
Observation 4: Residual connections are generally es- sential, except in shallow GNNs applied to small graphs
For large-scale datasets such as CIFAR 10, COCO-SP, and the OGB datasets, removing normalization leads to signifi- cant performance drops. Specifically, on ogbg-ppa, which has 158, 100 graphs, ablating normalization results in an accuracy drop of around 15% for three classic GNNs. This result is consistent with Luo et al. (2024b), who found that normalization is more important for GNNs in node clas- sification on large graphs. In such datasets, where node feature distributions are more complex, normalizing node
Removing residual connections generally leads signifi- cant performance drops across datasets, with the only excep- tions being found in the peptide datasets. Although similar in the number of nodes to CLUSTER and PATTERN, pep- tide datasets involve GNNs with only 3-5 layers, while the others use deeper networks with over 10 layers. For shallow networks in small graphs, residual connections may not be as beneficial and can even hurt performance by disrupting feature flow. In contrast, deeper networks in larger graphs
8

---

Can Classic GNNs Be Strong Basclines for Graph-level Tasks?
rely on residual connections to maintain gradient flow and enable stable, reliable long-range information exchange.
et al. (2023) further investigates LRGB and shows that clas- sic GNNs can close the gap with GT's under proper tuning. Grötschla et al. (2024) offers a systematic study of positional encoding. However, these studies have been constrained in both scope and comprehensivenes, primarily due to the limited number and diversity of datasets used, as well as an incomplete examination of GNN hyperparameters.
Observation 5: FFN is crucial for GIN and GCN greatly impacting their performance across datasets.
Ablating FFN leads to substantial performance declines for GIN and GCN+ across almost all datasets, highlighting its essential role in graph-level tasks. Notably, on MNIST, removing FFN leads an 88% relative accuracy drop for GIN This is likely because the architectures of GIN+ and GCN+ rely heavily on FFN for learning complex node fea- ture representations. In contrast, GatedGCN+ uses gating mechanisms to adaptively adjust the importance of neigh- boring nodes' information, reducing the need for additional feature transformations. The only exceptions are observed in the peptides datasets, where FFN not used in all three models. This may be due to the shallow GNN architecture, where complex feature transformations are less necessary.
7. Limitations and Future Work
While our study provides strong empirical evidence that enhanced classic GNNs can match or even surpass GTs in graph-level tasks on existing benchmarks, it is important to acknowledge that these findings are derived solely from empirical evaluations and lack a theoretical explanation for the observed advantage. Moreover, as highlighted in recent discussions (Bechler-Speicher et al., 2025), current graph datasets may not adequately capture the complexity and diversity inherent in real-world graph problems. Conse- quently, although our results demonstrate the competitive- ness of classic GNNs under current datasets and evaluation settings, their relative advantage may shift as more challeng- ing and application-driven benchmarks are developed.
Observation 6: PE is particularly effective for small- scale datasets, but negligible for large-scale datasets.
Removing PE significantly reduces performance for classic GNNs on small-scale datasets like ZINC, PATTERN, CLUS- TER, Peptides-fune and ogbg-molhin which only contain 10,000-40,000 4000 graphs. By contrast, on large-scale datasets like ogbg-code2, egbg-molpeba ogbg-ppat and COCO-SP (over 100,000 graphs), the impact of PE is less pronounced. This may be because smaller datasets rely more on PE to capture graph structure, whereas larger datasets benefit from the abundance of data, reducing the need for PE.
A deeper thcoretical understanding is needed to elucidate the role of each architectural component within GNN and their interactions, particularly regarding the model's 's expressive- ness, representational behavior, and its capacity to address oversmoothing and oversquashing issues. These directions represent promising avenues for future research.
8. Conclusion
6. Related Work
This study highlights the fften-overlooked potential of clas- sic GNNs in addressing graph-level tasks. By integrating six widely used techniques into a unified GNN* framework, we enhance three classic GNNs (GCN, GIN, and GatedGCN) for graph-level tasks. Comprehensive evaluations on 14 benchmark datasets reveal that these enhanced GNNs can match or even outperform SOTA GTs, while also demon- strating greater computational efficiency. These findings challenge the prevailing belief that GTs are inherently supe- rior, reaffirming the effectiveness of simple GNN structures as powerful models for graph-level tasks.
Our work relates closely to recent efforts in benchmarking GNNs. As node-level benchmarks have received signifi- cantly more attention, several studies such as (Shchur et al., 2018; Wang et al., 2021; Lv al.. 2021: Platonov et al., 2023) have examined the reliability of existing evaluation protocols, revealing that many newly proposed architectures fail to outperform simple baselines under fair conditions. Building on this line of inquiry, Luo et al. (2024b) rigorously showed that, with the same hyperparameter search space, classic GNNs can match or even surpass SOTA models across 18 widely used node classification datasets.
In contrast, our study targets graph-level tasks. Errica al. (2020) highlights the importance of rigorous evaluation pro- tocols and shows that many claimed improvements van- ish under fair settings. Hu et al. (2020) introduces the OGB benchmark to standardize large-scale graph evalu- ations. Dwivedi et al. (2023) introduces diverse suite of graphs for fair architectural comparisons under fixed param- eter budgets, and Dwivedi et al. (2022) proposes the LRGB to assess models on long-range dependency tasks. Tönshoff
A Acknowledgments
We sincerely thank the anonymous area chair for their thoughtful evaluation and strong support, which gave us the opportunity to share this work with the community. We are also grateful to all anonymous reviewers for their con- structive and insightful feedback. We sincerely thank Yiwen Sun for her invaluable help with refining the writing and Lu Fan for her help with creating Figure 1.
9

---

Can Classic GNNS Be Strong Basclines for Graph-level Tasks?
This work received support from National Key R&D Pro- gram of China (2021 YFB3500700), NSFC Grant 62172026, National Social Science Fund of China 22&ZD153, the Fundamental Research Funds for the Central Universities, State Key Laboratory of Complex & Critical Software Envi- romment (SKLCCSE), and the HK PolyU Grant P0051029. Lei Shi is with School of Computer Science and Engineer- ing, Beihang University, and the State Key Laboratory of Complex & Critical Software Environment.
Cai, C. and Wang. Y. A simple yet effective baseline for non-attributed graph classification. arXiv preprint arXiv:181 1..05800.0.. 2018.
Cai, T., Luo, S., Xu, K., He, D., Liu, T.y., and Wang. Graphnorm: principled approach to accelerating graph neural network training. In International Conference on Machine Learning, pp. 1204-1215, PMLR, 2021.
Chen, D., Lin, Y., Li, W., Li, P., Zhou, J., and Sun, X. Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In Proceedings of the AAAI Conference Artificial Intelli- gence, volume 34, pp. 1438-34455 2020.
Impact Statement
This paper presents an empirical benchmarking study that re- evaluates the performance of classic GNNs on graph-level tasks. By systematically integrating widely used techniques into a unified framework (GNN+), the study demonstrates that classic GNNs can achieve performance comparable to SOTA models. The findings challenge common assumptions in the field and provide practical guidance for future model selection and design. As this is benchmarking effort, we do not foresee any immediate socictal consequences that must be specifically highlighted.
Chen, D., O'Bray, L.., and Borgwardt, K. Structure-aware transformer for graph representation learning. Interna- tional Conference Machine Learning, pp. 1469-34899 PMLR, 2022.
Chen, J., Gao, K., Li, G., and He, K. NAGphormer: tokenized graph transformer for node classification in large graphs. In The Eleventh International Confer- ence Learning Representations, 2023a. URL https: wwtp://e/oooommmidddKkoaao
References
Chen, Z., Tan, H., Wang, T., Shen, T., Lu, T. Peng. Q., Cheng, C., and Qi, Y. Graph propagation trans- former for graph representation learning. arXiv preprint arXiv:2305. 1424, 2023b.
Alon, U. and Yahav, E. On the bottleneck of graph neural networks and its practical implications. arXiv preprint 1oXiv:2006.05205 2020.
Ba, J. L., Kiros, R., and Hinton, G. E. Layer normalization. arXiv preprint arXiv:1607.06455 2016.
Choi, Y. Y., Park, S. W., Lee, M., and Woo, Topology-informee graph transformer. arXiv preprint arXi::240..02005, 2024.
Bar-Shaloma G., Bevilacqua, B., and Maron, H. Sub- graphormer: Unifying subgraph gnns and graph transformers via graph products. arXiv preprint arXiv:2402.084500 2024.
Ding, Y., Orvieto, A., He, B., and Hofmann, T. Recurrent distance-encodee neural networks for graph representa- tion learning, 2024. URL https://peenevieewnefffoonm id=INIjSFdXsC.
Bechler-Speicher, M., Finkelshtein, B., Frasca, F., Müller, L., Tönshoff, J., Siraudin, A., Zaverkin, V.. Bronstein, M., Niepert, M., Perozzi, B., al. Position: Graph learning will lose relevance due to poor benchmarks. arXiv preprint arXhv:2502.14546, 2025.
Duvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bom- barell, R., Hirzel, T., Aspuru-Guzike A., and Adams, P. Convolutional networks on graphs for learning molecular fingerprints. Advances in neural information processing systems, 28, 2015.
Behrouz, A. and Hashemi, F. Graph mamba: Towards learn- ing graphs with state space models. In Proceedings of the 30th ACM SIGKDD Conference Knowledge Discovery and Data Mining, pp. 119-130, 2024.
Dwivedi, P. and Bresson, of transformer networks graphs. arXiv preprint arXiv:20/2.09699 2020.
generalization
Dwivedi, P., Luu, A. T., Laurent, T., Bengio, Y., and Bres- son, X. Graph neural networks with learnable struetural and positional representations. International Confer- ence Learning Representations, 2021.
Bo, D., Shi, C., Wang, L., and Liao, R. Specformer: Spectral graph neural networks meet transformers. arXiv preprint arXiv:2303.01002 2023.
Dwivedi, V. P., Rampášek, L., Galkin, M., Parviz, A., Wolf, G., Luu, A. T., and Beaini, D. Long range graph bench- mark. arXiv preprint arXiv:2206 006.6.0864.....4 2022.
Bresson, X. and Laurent, T. Residual gated graph convnets. arXiv preprint 17 2017.
10

---

Can Classic GNNs Be Strong Basclines for Graph-level Tasks?
Dwivedi, V. P., Joshi, C. K., Luu, A. T., Laurent, T., Ben- gio, Y., and Bresson, X. Benchmarking graph neural networks. Journal of Machine Learning Research, 24 (43):1-48, 2023.
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovee, J. Strategies for pre-training graph neural networks. arXiv preprint arXiv: (ttl:::955.220000 2019.
Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J. Open graph benchmark: Datasets for machine learning graphs. Advances in neural information processing systems, 13:22118-22133 2020.
Errica, F., Podda, M., Bacciu, D., and Micheli, A. fair comparison of graph neural networks for graph classifica- tion. In International Conference Learning Represen- lations, 2020.
Fey, M. and Lenssen, Fast graph representation learning with pytorch geometric. arXiv preprini arXiv: 1903.0228,, 2019.
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q. Densely connected convolutional networks. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 7700-4778,, 2017.
Finkelshtein, B., Huang. X., Bronstein, M. M., and Ceylan, I. Cooperative graph neural networks. In Forty-first International Conference on Machine Learning, 2024.
Huang, S., Song, Y., Zhou, J., and Lin, Cluster-wint graph transformer with dual-granularity kernelized at- tention. In The Thirty-eighth Annual Conference Neural Information Processing Systems, 2024a. URL http:////oeeeeeeee.eeeoommm//ddp
Freitas, S. and Dong, Y. A lcrge-scale database for graph representation learning. Advances in neural information processing systems, 2021.
Huang, Y., Miao, S., and Li, P. What can learn from state space models for machine learning graphs? arXiv preprint arXiv:2406.005: 05815 2024b.
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E. Neural message passing for quantum chem- istry. In International conference on machine learning, pp. 1263-12722 PMLR, 2017.
Hussain, M. S., Zaki, M. J., and Subramanian, D. Global self-attention as a replacement for graph convolution. In Proceedings of the 28th ACM SIGKDD Conference Knowledge Discovery and Data Mining, pp. 655-665, 2022.
Grötschla, F., Xie, J., and Wattenhofer, R. Benchmarking positional encodings for gnns and graph transformers. arXiv preprint ar 2rXhv2241..1273.. 2024.
Gutteridge, B., Dong, X., Bronstein, M. M., and Di Gio- vanni, F. Drew: Dynamically rewired message pass- ing with delay. In International Conference on Machine Learning, pp. 1222-112661 PMLR, 2023.
loffe. and Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference machine learning, pp. 448- 456. pmlr, 2015.
Hamilton, W., Ying, Z. and Leskovec, J. Inductive repre- sentation learning on large graphs. Advances in neural information processing systems, 30, 2017.
Kipf, T. N. and Welling, M. Semi-supervised classifica- tion with graph convolutional networks. In International Conference Learning Representations, 2017. URL
He, K., Zhang, X., Ren, S., and Sun, J. Deep residual leam- ing for image recognition. In Proceedings of the IEEE conference computer vision and puttern recognition, pp. 770-778, 2016.
Kreuzer, D., Beaini, D., Hamilton, W., Létourneau, V., and Tossou, P. Rethinking graph transformers with spectral attention. Advances in Neural Information Processing Systems, 34:21618-2166 2021.
He, X., Hooi, B.. Laurent, T., Perold, A., LeCun, Y., and Bresson, X. A generalization of vit/mlp-mixer to graphs. In International conference machine learning, pp. 12724-12745. PMLR, 2023.
Li, G., Muller, M., Thabet, A., and Ghanem, Deepgens: Can gens go as deep cnns? In Proceedings of the IEEE/CVF international conference on computer vision, pp. 9267-9276, 2019.
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv: 1207.00000000880 2012.
Li, P., Wang, Y., Wang, H., and Leskovec, J. Distance en- coding: Design provably more powerful neural networks for graph representation learning. Advances in Neural Information Processing Systems, 33:4465-444 2020.
Hoang, T. Lee, O., et al. A survey on suuucture-prsservie graph transformers. arXiv preprint arXiv:2401.16176, 2024.
Li, Q., Han, Z., and Wu, X.-M. Deeper insights into graph convolutional networks for semi-supervised learning. In

---

Can Classic GNNS Be Strong Basclines for Graph-level Tasks?
Thirty-Second AAAI conference on artificial intelligence, 2018.
Lv, Q. Ding, M., Liu, Q.. Chen, Y., Feng, W., He, S., Zhou, C., Jiang, J., Dong, Y., and Tang, J. Are really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks. In Proceedings of the 27th ACM SIGKDD conference on knowledge discov- ery & data mining, pp. 1150-1160, 2021.
Liang, J., Chen, M., and Liang, Graph external attention enhanced transformer. arXiv preprint arXiv:2405.2106) 2024.
Lin, C., Ma, L., Chen, Y., Ouyang, W. Bronstein, M. M., and Torr, P. Understanding graph transformers by gen- eralized propagation, 2024. URL, https///openreview.nee forum"id-JfjduOxxYY.
Ma. L., Lin, C., Lim, D., Romero-Soriano, A., Dokania, P. K., Coates, M., Torr, P., and Lim, S.-N. Graph inductive biases in transformers without message passing. arXiv preprint arXiv:2305. 2023.
Loshchilov, I. Decoupled weight decay regularization. arXiv preprint arXiv:1711.0510. 2017.
Min, E., Chen, R., Bian, Y., Xu, T., Zhao, K., Huang, W. Zhao, P., Huang, J., Ananiadou, S., and Rong, Y. Trans- former for graphs: An overview from architecture per- spective. arXiv preprint arXiv:2202.08455, 2022.
Luo, S., Li, S., Zheng, S., Liu, T.-Y., Wang, L., and He, D. Your transformer may not be as powerful you expect. Advances in Neural Information Processing Systems, 35; 4301-433 3301-4315,-5 2022.
Morris, C.,, Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., and Grohe, M. Weisfeiler and leman go neural: Higher-order graph neural networks. In Pro- ceedings of the AAAI conference on artificial intelligence, volume 33, pp. 4602-4609, 2019.
Luo, Y., Shi, L., and Thost, V. Improving self-supervised molecular representation learning using persistent homol- ogy. In seirth- Conference on Neural Information Processing Systems, 2023a. URL https://ppeneeiiew.nedl forummdd--EBUUGperMM...
Morris, C., Kriege, N. M., Bause, F., Kersting, K., Mutzel, P., and Neumann, M. Tudataset: A collection of bench- mark datasets for learning with graphs. arXiv preprint 2007:20077.8863. 2020.
Luo, Y. Shi, L., Xu, M., Ji, Y., Xiao, F., Hu, C., and Shan, Z. Impact-oriented contextual scholar profiling using self-citation graphs. arXiv preprint arXiv:2304.12217, 2023b.
Müller, L., Galkin, M., Morris, C., and Ramp&šek, L. Attending graph transformers. arXiv preprint arXiv:2302.04181, 2023.
Luo. Y., Li, H., Shi, L., and Wu, X.-M. Enhancing graph transformers with hierarchical distance structural encoding, In The Thirty- eighth Annual Conference on Neural Information Processing Systems, 2024a. URL. http://pwwwwootteeee////oomnoddd
Ngo, N. K., Hy, T. S.,, and Kondor, R. Multiresolution graph transformers and wavelet positional encoding for learning long-range and hierarchical structures. The Journal of Chemical Physics, 159(3), 2023.
Luo, Y., Shi, L.., and Wu, X.-M. Classic GNNs are strong baselines: Reassessing GNNs for node classification. In The Tiihty-eight Conference on Neural Information Pro- cessing Systems Datasets and Benchmarks Track, 2024b. URL http:///weeeeeeeeeeeee/o//dddddd
Niepert, M., Ahmed, M., and Kutzkov, K. Learning con- volutional neural networks for graphs. In International conference on machine learning, pp. 2014-2023. PMLR, 2016.
Luo, Y., Thost, V., and Shi, L. Transformers over directed acyclic graphs. Advances in Neural Information Process- ing Systems, 36, 2024c.
Park, W., Chang, W., Lee, D., Kim, J., and Hwang, S.-w. Grpe: Relative positional encoding for graph transformer. arXiv preprint
2022.
Luo. Y., Li, H., Liu, Q. Shi. L., and Wu, X.-M. Node identifiers: Compact, discrete representations for effi- cient graph learning. In The Thirteenth International Conference on Learning Representations, 2025a. URL http://ppweeeeewwee////mmdddd/dd
Pei, H., Li, Y., Deng, H., Hai, J., Wang, P., Ma, J., Tao, J., Xiong, Y., and Guan, X. Multi-track message pass- ing: tackling oversmoothing and oversquashing in graph learning via preventing heterophily mixing. In Forty-firt International Conference on Machine Learning, 2024.
Luo, Y., Wu, X.-M., and Zhu, H. Beyond random masking: When dropout meets graph convolutional networks. In The Thirteenth International Conference on Learning Representations, 2025b. URL https://ppeneviee.nee// YoMvmvy. Yowyyyyy..
Platonov, O., Kuznedelev, D., Diskin, M., Babenko, A., and Prokhorenkova, L.. A critical look at the evaluation of gnns under heterophily: Are we really making progress? arXiv preprint arXiv:2302.1 1640, 2023.
12

---

Can Classic GNNs Be Strong Basclines for Graph-level Tasks?
Rampášek, L., Galkin, M., Dwivedi, V. P., Lau, A. T., Wolf, G., and Beaini, D. Recipe for general, powerful, seal- able graph transformer. arXiv preprint arXiv:2205.1245x 2022.
Wu, J., Li, S., Li, Pan, Y., and Xu, K. A simple yet effective method for graph classification. arXiv preprint arXiv:2206.022 02404, 2022.
Wu, Q. Yang. C., Zhao, W., He, Y., Wipf, D., and Yan, J. DIFFormer: Scalable (graph) transformers induced by en- ergy constrained diffusion. In The Eleventh International Conference Learning Representations, 2023. URL https///ooonreiieveeeffoommmdddd
Sancak, K., Hua, Z., Fang, J., Xie, Y., Malevich, A., Long, B., Balin, M. F., and Çatalyürek, Ü. V. A scalable and effeecive alternative to graph transformers. arXiv preprint arXiv:2406. / /2059, 2024.
Shchur, O., Mumme, M., Bojchevski, A., and Günnemann,
Wu, Z.,, Pan, S., Chen, F., Long, G., Zhang, C., and Philip. S. Y. comprehensive survey on graph neural networks. IEEE transactions neural networks and learning sys- tems, 32(1):4-24 2020.
S. Pitfalls of graph neural network evaluation. arXiv 2018.
preprint
Shehzad, A., Xia, F., Abid, S., Peng, C., Yu, S., Zhang, D., and Verspoor, K. Graph transformers: A survey. arXiv preprint ar Xiv:2447.00977, 2024.
Wu, Z., Jain, P., Wright, M., Mirhoseini, A., Gonzalez, J. E., and Stoica, Representing long-range context for graph neural networks with global attention. Advances in Neural Information Processing Systems, 34:13266- 13279, 2021.
Shirzad, H., Velingker, A.. Venkatachalam, B., Sutherland, D. J., and Sinop, A. K. Exphormer: Sparse transformers for graphs. arXiv preprint arXiv:2303.061477 2023.
Xu, K., Hu, W., Leskovec, J., and Jegelka, S. How powerful are graph neural networks? arXiv preprint arXiv: 181 1810.00826, 2018.
Shu, J. Xi, B., Li, Y., Wu, F,, Kamhoua, C., and Ma, J. Understanding dropout for graph neural networks. In Companion Proceedings of the Web Conference 2022, pp. 1128-1138, 2022.
Yang, Z., Cohen, W., and Salakhudinov, R. Revisiting semi-supervised learning with graph embeddings. In International conference machine learning, pp. 40-48. PMLR, 2016.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I.,
and Salakhutdinov, Dropout: simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1):1929-1988 2014.
Yin, S. and Zhong, Lgi-gt: Graph transformers with local and global operators interleaving. 2023.
Tang, J., Sun, J., Wang, C., and Yang. Z. Social influence analysis in large-scale networks. In Proceedings of the 15th ACM SIGKDD international conference Knowl- edge discovery and data mining, pp. 807-816, 2009.
Ying, C., Cai, T., Luo, S., Zheng. S., Ke, G., He, D., Shen, Y., and Liu, T.-Y. Do transformers really perform badly for graph representation? Advances in Neural Information Processing Systems, 34:28877-28888 2021.
Tönshoff, J., Ritzert, M., Rosenbluth, E., and Grohe, M. Where did the gap go? reassessing the long-range graph 2023.
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. How trans- ferable are features in deep neural networks? Advances neural information processing systems, 27, 2014.
benchmark. arXiv preprint
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. At- tention is all you need. Advances in neural information processing systems, 30, 2017.
Yun, S., Jeong, M., Kim, R., Kang, J., and Kim, H. Graph transformer networks. Advances in neural information processing systems, 32, 2019.
Veličković. P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y. Graph attention networks. In International Conference on Learning Representations, 2018.
Zhang, B., Luo, S., Wang, L., and He, D. Rethinking the expressive power of GNNs via graph biconnectivity. In The Eleventh International Conference on Learning Rep- resentations, 2023. URL https//openeviee.neffoomm id=r9hNv76KoT3.
Wang, C., Tsepa, O., Ma, J., and Wang, B. Graph-mame :aaph- Towards long-range graph sequence modeling with se- lective state spaces. arXiv preprint arXiv:2402.00789, 2024.
Zhang, J., Zhang, H., Xia, C., and Sun, L. Graph-bert: Only attention is needed for learning graph representations. arXiv preprint arXiv:2001.05 1,00000,,,,,,, 2020.
Wang, Y., Jin, J., Zhang, W., Yu, Y., Zhang, Z., and Wipf, D. Bag of tricks for node classification with graph neural networks. arXiv preprint arXiv:2100 13355, 2021.
13

---

Can Classic GNNs Be Strong Basclines for Graph-level Tasks?
A. Datasets and Experimental Details
A.1. Computing Environment
Our implementation is based on PyG (Fey & Lenssen, 2019). The experiments are conducted on a single workstation with
RTX 3090 GPUs.
A.2. Datasets
Table 7 presents a summary of the statistics and characteristics of the datasets.
GNN Benchmark (Dwivedi et al., 2023). ZINC contains molecular graphs with node features representing atoms and edge features representing bonds The task to regress the constrained solubility (logP) of the molecule. MNIST and CIFAR1O are adapted from image classification datasets, where each image is represented as an 8-nearest-neighhor graph of SLIC superpixels, with nodes representing superpixels and edges representing spatial relationships. The 10-class classification tasks follow the original image classification tasks. PATTERN and CLUSTER are synthetic datasets sampled from the Stochastic Block Model (SBM) for inductive node classification, with tasks involving sub-graph pattern recognition and cluster ID inference. For all datasets, we adhere to the respective training protocols and standard evaluation splits (Dwivedi et al., 2023).
Long-Range Graph Benchmark (LRGB) (Dwivedi et al., 2022; Freitas & Dong. 2021). Peptides-func and Peptides- struct are atomic graphs of peptides from SATPdb, with tasks of multi-label graph classification into peptide functional classes and graph regression for 11 3D structural properties, respectively. PascalVOC-SP and COCO-SP are node classification datasets derived from the Pascal VOC and MS COCO images by SLIC superpixelization, where each superpixel node belongs to particular object class. We did not use PCQM-Contact in (Dwivedi et al., 2022) as its download link was no longer valid. MalNet-Tiny (Freitas & Dong, 2021) is subset of MalNet with 5,000 function call graphs (FCGs) from Android APKs, where the task is to predict software type based on structure alone. For each dataset,
we follow standard training protocols and splits (Dwivedi et al., 2022; Freitas & Dong. 2021).
Open Graph Benchmark (OGB) (Hu et al., 2020). We also consider a collection larger-scale datasets from OGB, containing graphs in the range of hundreds of thousands millions: ogbg-molhiv and ogbg-molpeba are molecular property prediction datasets from MoleculeNet. ogbg-molhiv involves binary classification of HIV inhibition, while ogbg-molpcba predicts results of 128 bioassays in multi-task setting, ogbg-ppa ppa contains protein-protein association networks, where nodes represent proteins and edges encode normalized associations between them; the task is to classify the origin of the network among 37 taxonomic groups ogbg-code2 consists of abstract syntax trees (ASTs) from Python source code, with the task of predicting the first subtokens of the function's name. We maintain all the OGB standard evaluation settings (Hu et al., 2020).
Table Overview of the datasets used for graph- level tasks (Dwivedi al., 2023; 2022; Hu al., 2020; Freitas & Dong, 2021).
Dataset
graphs
Avg. nodes
Avg. edges
node/edge feats
Prediction level
Prediction task
Metric
ZINC
12,000
23.2
24.9
28/1
graph
regression
MAE
MNIST
70,000
70.6
564.5
3/1
graph graph
10-class classif.
Accuracy
CIFARIO
60,000
117.6
941.1
5/1
10-class classif.
Accuracy
PATTERN
14,000
118.9
3,039.3
3/1
inductive node
hinary classif.
Accuracy
CLUSTER
12,000
117.2
2,150.9
7/1
inductive node
6-class classif.
Accuracy
Peptides-fune
15,535
150.9
307.3
9/3
graph graph
10-task classif.
Avg. Precision
Poptides-struct
15,535
150.9
307.3
9/3
11-task regression
MAE
PascalVOC-SP
11,355
479.4
2,710.5
14/2
inductive node
21 21-class classif.
score
COCO-SP
123,286
476.9
2,693.7
14/2
inductive node
81-class classif.
score
MalNet-Tiny
5,000
1,410.3
2,859.9
5/1
graph
5-class classif.
Accuracy
oghg-molhiv
41,127
25.5
27.5
9/3
graph
hinary elassif.
AUROC
ogbg-molpcha
437,929
26.0
28.1
9/3
graph
128-task classif.
Avg. Precision
ogbg-ppa
158
243.4
2,266.
1/7
graph 37-task classif.
Accuracy
ogbg-code2 452,741
125.2
124.2
2/2
graph token sequence
Fl score
A.3. Hyperparameters and Reproducibility
Please note that we mainly follow the experiment settings of GraphGPS (Rampášek et al., 2022; Tönshoff et al., 2023). For the hyperparameter selections of classic GNNs, in addition to what we have covered, we list other settings in Tables 8, 9, 10,
14

---

Can Classic GNNs Be Strong Baselines for Graph-level Tasks?
11, 12, 13. Further details regarding hyperparameters can be found in code.
In all experiments, the GNN model.
the validation set to select the best hyperparameters. GNN+ denotes enhanced implementation of
Our code is available under the MIT License.
Table 8. Hyperparameter settings GCN on benchmarks from (Dwivedi al., 2023).
Hyperparameter
ZINC
MNIST
CIFARIO
PATTERN
CLUSTER
# GNN Layers
12
6
5
12
12
Edge Feature Module
True
True
True
True
False
Normalization
BN
BN
BN
BN
BN
Dropout
0.0
0.15
0.05
0.05
0.1
Residual Connections
True
True
True
True
True
FFN
True
True
True
True
True
PE
RWSE-32
False
False
RWSE-32
RWSE-20
Hidden Dim
64
60
65
90
90
Graph Pooling
add
mean
mean
Batch Size
32
16
16
32
16
Learning Rate
0.001
0.0005
0.001
0.001
0.001
# Epochs
2000
200
200
200
100
# Warmup Epochs
50
5
5
5
5
Weight Decay
le-5
le-5
1e-5
1e-5
1e-5
# Parameters
260,177
112,570
114,345
517.219
516,674
Time (epoch)
7.6s
60.1s
40.2s
19.5s
29.7s
Table Hyperparameter settings of GCN on LRGB and OGB datasets.
Hyperparameter
Peptides-fune
Peptides-struct
PascalVOC-SP
COCO-SP
MalNet-Tiny
ogbg-molhiv
ogbg-molpeha
ogbg-ppa
ogbg-code2
# GNN Layers
3
5
14
4
Edge Feature Module
True
False
True
Truc
True
Truc
True
True
Truc
Normalization
BN
BN
BN
BN
BN
BN
BN
BN
BN
Dropout
0.2
0.2
0.0
0.1
Residual Connections
False
False
True
False
True
True
FFN
False
False
True
True
Thue
True
True
True
True
PE
RWSE-32
RWSE-32
False
False
False
RWSE-20
RWSE-16
False
False
Hidden Dim
275
255
85
70
110
256
512
512
512
Graph Pooling
mean
mean
max
mean
mean
mean
mean
Batch
16
32
32
Learning
0.001
0.001
0.001
0.001
0.0005
0.0001
0.0005
0.0003
0.0001
# Epochs
300
300
200
300
150
100
100
400
30
# Warmup Epochs
5
5
10
10
10
5
10
Weight Decay
0.0
0.0
le.5
1e-5
1e-5
16-5
1e-6
# Parameters
507.351
506.127
520,986
460.611
494,235
1,407,641
13,316,700
5,549.605
23,2918826
Time (epoch)
6.9s
6.6s
12.5s
162.5s
16.3s
91.4s
178.2s
476.36
15

---

Can Classic GNNs Be Strong Baselines for Graph-level Tasks?
Table 10. Hyperparameter settings of GIN on benchmarks from (Dwivedi et al., 2023).
Hyperparameter
ZINC
MNIST
CIFARIO
PATTERN
CLUSTER
# GNN Layers
12
5
5
8
10
Edge Feature Module
True
True
True
True
True
Normalization
BN
BN
BN
Dropout
0.0
0.1
0.05
0.05
0.05
Residual Connections
True
Truc
True
True
True
FFN
True
Truc
True
True
Truc
PE
RWSE-20
False
False
RWSE-32
RWSE-20
Hidden Dim
60
60
100
Graph Pooling
sum
mean
mean
Batch Size
16
16
32
Learning Rate
0.001
0.001
0.001
0.001
0.0005
# Epochs
2000
200
200
200
100
Warmup Epochs
50
5
5
5
Weight Decay
le-5
le-5
le-5
le-5
le-5
Parameters
477,241
118,990
115,450
511,829
497,594
Time (epoch)
9.4s
56.8s
46.3s
18.5s
20.5s
Table II, Hyperparameter settings of GIN on LRGB and OGB datascts.
Hyperparameter
Peptides-
Peptides-struct
PascalVOC-SP
COCO-SP
MalNet-Tiny
ogbg-molhiv
oghg-molpeha
ogbg-ppa
ogbg-code2
# GNN Layers
3
5
3
Edge Feature Module
True
True
True
True
Normalization
BN
BN
BN
BN
BN
BN
Dropout
0.2
0.2
0.1
0.0
0.0
0.0
0.3
0.15
0.1
Residual Connections
True
True
True
Thue
True
True
True
False
True
FFN
False
False
True
True
True
False
True
True
True
PE
RWSE-32
RWSE-32
RWSE-32
False
False
RWSE-20
RWSE-16
False
Falso
Hidden Dim
240
200
70
70
130
256
300
512
512
Graph Pooling
mean
mean
max
mean
mean
mean
mean
Batch Size
16
32
50
50
16
32
512
32
32
Learning Rate
0.0005
0.001
0.001
0.001
0.0005
0.0001
0.0005
0.0003
0.0001
Fpaccs
# Warmup Fpochs
5
5
10
5
10
Weight Decay
0.0
0.0
0.0
0.0
lo-5
lc-5
1c-5
16-5
lc-6
# Parameters
506, 126
518,127
486,039
487,491
514,545
481,433
8,774,720
8,173,605
24,338,354
Time (epoch)
7.4s
6.1s
14.8s
169.2s
5.9
10.9s
89.2s
213.9s
489.8s
16

---

Can Classic GNNs Be Strong Basclines for Graph-level Tasks?
Table 12. Hyperparameter settings of GatedGCN on benchmarks from (Dwivedi et al., 2023).
Hyperparameter
ZINC
MNIST
CIFARIO
PATTERN
CLUSTER
GNN Layers
9
10
10
12
16
Edge Feature Module
True
True
True
True
True
Normalization
BN
BN
BN
BN
Dropout
0.05
0.05
0.15
0.2
0.2
Residual Connections
True
True
True
True
True
FFN
True
True
True
Truc
Truc
RWSE-20
False
False
RWSE-32
RWSE-20
Hidden Dim
70
35
64
56
Graph Pooling
sum
mean
mean
Batch Size
32
32
16
Learning Rate
0.001
0.001
0.001
0.0005
0.0005
Epochs
2000
200
200
200
100
Warmup Epochs
50
5
5
5
Weight Decay
le-5
le-5
le-5
le-5
le-5
Parameters
413,355
118,940
116,490
466,001
474,574
Time (epoch)
10.5s
137.9s
115.0s
32.6s
34.1s
Table 13. Hyperparameter settings of GatedGCN+ on LRGB and OGB datasets.
Hyperparameter
Peptides-fure func
Peptides-stmis strict
PS
COCO-SP
MalNet-Tiny
oghg molhiv
oghg molpeha
oghg-ppa
oghg-code?
# GNN Layers
5
12
3
Edge Feature Module
True
True
True
True
Normalization
BN
BN
BN
BN
BN
BN
BN
BN
BN
Dropout
0.05
0.2
0.15
0.05
0.0
0.0
0.2
0.15
0.2
Residual Connections
False
True
True
Thue
True
Truc
True
True
Truc
FFN
False
False
Fulse
True
True
False
True
False
True
RWSE-32
RWSE-32
RWSE-32
False
False
RWSE-20
RWSE-16
False
False
Hidden Dim
135
145
95
100
256
256
512
512
Graph Pooling
mean
mean
max
mean
mean
mean
mean
Batch Size
16
32
32
50
16
32
512
32
32
Learning Rate
0.0005
0.001
0.001
0.001
0.0005
0.0001
0.0005
0.0003
0.0001
# Epochs
300
300
150
100
# Warmup Epochs
5
10
5
10
Weight Decay
0.0
0.0
0.0
0.0
le:5
le-5
le-5
le:5
1e-6
# Parameters
521,141
492,897
559,094
508.589
550,905
1,076,633
6,016,860
5,547,557
29,865 906
Time (epoch)
17.3a
8.0s
21.3s
208.8s
8.9s
15.1s
85.1s
479.8s
640.1s
B. Additional Experimental Results
B.1. Hyperparameter Sensitivity Analysis
To further understand the robustness of GNN+ we conduct a comprehensive sensitivity analysis focusing on two key hyperparameters: the number of layers and the dropout rate.
Layer Depth. We investigate the impact of the number of network layers on three datasets: PATTERN and CLUSTER, and VOS-SI As shown in Figure 3, GNN+ achieves strong and stable performance across a wide depth range. On PATTERN, both GCN+ and GatedGCN+ reach peak performance at 12 layers. In contrast, on CLUSTER and PascalVOC- SP, performance continues improve the number of layers increases, suggesting that GNN is capable of leveraging deeper architectures when appropriate.
Importantly, when residual connections are disabled (bottom row), we observe significant performance degradation as the number layers increases, especially beyond 8 layers. This most pronounced on CLUSTER and Pascal where over-smoothing and ver-squashing effects become severe in the absence of residual pathways. These results confirm that residual connections play a crucial role in stabilizing deep message propagation in GNN+
17

---

Can Classic GNNs Be Strong Baselines for Graph-level Tasks?
Tist Accuracy of ENki BATTEW
0.43
13700
0.00
3.13
0.000
3.00
0.0.5
3.0
025
025
1.000
020
Nartor Leyers
Eayers
Memter Llown
1:1 Accuracy of GNK) whthot dosiuaa Connections on PATTERN
Test Accuracy GTGN witrout Resiiua Cornectiors on CLUSTER
Sol 72 Score of GNB without Seidual Cornectiora en PacahvOCCP
3.013
5/33
3.55
3.05
3200
Number L'yeer
Figure 3. Experiments on the impact of the number of layers in GNN+ on PATTERN, CLUSTER, and PascalVOC-SSP Top row:
performance with residual connections; bottom row: performance without residual connections.
Dropout Rate. We conduct additional experiments to analyze the effect of dropout rates (ranging from 0.0 to 0.5) on four datasets: CLUSTER, PascalVOC-SP, Peptides-struct, and Peptides-func. As shown in Figure 4, the optimal dropout rate lies in the range of 0.1 to 0.2. Performance degrades significantly when the dropout rate reaches 0.3 or higher across all datasets. For instance, on Peptides-struct, the MAE increases sharply beyond 0.2 dropout, and on CLUSTER, accuracy collapses at 0.5. This suggests that excessive dropout disrupts the message-passing process in graph-level tasks.
Binary Hyperparameters. For components that can be enabled or disabled, such as edge features, normalization, residual connections, FFN, and positional encoding, we refer readers to the ablation results in Tables and 6.
B.2. Performance of a Fixed GNN+ Configuration
We conduct an additional experiment using a fixed architecture of GNN+ in which all six techniques are integrated. This model referred to as GNN+ (fixed) is applied uniformly across datasets without dataset-specific tuning. The results are summarized in Table 14. The performance of GNN+ (fixed) is comparable to the best configurations reported in the main paper, further demonstrating the model's practicality and robustness without requiring extensive tuning.
Table 14. Performance GNN+
with fixed configuration (i.c., all six techniques integrated) across datasets.
PascalVOC-SP COCO-SP
MaiNet-Tiny oghg-molpcha
oghg-codd2
MNIST
CIFARIO
PATTERN
CLUSTER
GCN
0.3357 1o0m
0.2733 1ore 0.9354
nous
0.2721
1o0
0.1787 arms
98.382 noss
69.824
87.021 ams
77.109 1mm
GCN (fixed)
0.3341 1000s
0.27162nwe
0.9235
0090 0.2694
6m0
0.1 1784 77744=mm arce
98.257
0063
69.436
87.021
ams
76.352 n757
GatedGCN+
0.4263 +
0.3802 +
0.9460
50057
0.2981
0,1896
98.712
77.218
87.029
79.128
GatedGCN (fixed)
0.4204 + no06)
0.3774 0.00s
0.9450
0.2981
0/024
0.1889 o.00s
98.712 6:37
77.218
035)
87.029
oner 79.128
B.3. Additional Baselines
Comparison with Fast Linear Models. To assess the performance-effiicee trade-off, we compare GCN+ with two recent linear-time graph classification baselines, LDP (Cai & Wang, 2018) and HRN (Wu et al., 2022), on both small-scale and large-scale datasets, using the same training protocols and hyperparameter search spaces.
We first evaluate all models on five small-scale graph classification datasets used in (Cai & Wang, 2018; Wu et al., 2022): IMDB-B, B, IMDB-M, COLLAB, MUTAG, and PTC. These datasets contain between 300 and 5,000 graphs. As shown in Table 15, GCN+ achieves accuracy comparable to or exceeding that of LDP and HRN on all datasets. Given the small scale,
18

---

Can Classic GNNs Be Strong Baselines for Graph-level Tasks?
Dropout Sensitivity Analysis of GNN+ CLUSTER
Dropout Sensitivity Analysis GNN+ on PascalVOC-SP
0.6
0.42
0.7
0.6
0.36
0.5
0.34
0.4
0.32
0.3
0.30
Drapost Rata
Dropout
Dropout Sensitivity Analysis GNN+ Peptides-struct
Dropout Sensitivity Analysis GNN+ Peptides func
0.220
0051
0.282
0.200
0.25
0.80
0.59
0.0
0.3
0.2
6.3
64
0.5
0.
61
6.3
03
04
0.5
Drocout
Figure 4. Sensitivity analysis of dropout rates in GNN+ on CLUSTER, VOC-SS. Peptides-struct, and Peptides-func.
training times are negligible across models.
Table 15. Accuracy (%) small-scale graph classification datasets.
IMDB-B
IMDB-M
COLLAB
MUTAG
PTC
# graphs
1000
1500
5000
188
344
LDP HRN
75,4
50.0
78.1
90.3
64.5
77.5
52.8
81.8
90.4
65.7
GCN+ +
76.9
52.3
80.9
90.1
66.6
To examine scalability, we evaluate models on two OGB datasets: ogbg-molhiv and ogbg-molpeba. While LDP and HRN exhibit lower training costs, their predictive performance is significantly lower than GCN+ Table 16 summarizes this trade-off, demonstrating the importance of benchmarking large datasets for assessing practical utility.
Table 16. Performance and training cost OGB molecular property prediction datasets.
egbg-molhit
ogbg-molpeba
AUROC1 f Time (slepoch)
Avg. Precision f Time (s/epoch) 4
LDP
0.7121 + 0000s
0.1243 a0esi
HRN
0.7587 # 00147
0,2274 0004s
68
GCN+
0.8012 0.0124
16
0.2721 00046
91
Comparison with SOTA GNNs for Over-smoothing and Over-squashing. We further compare GCN+ with recently proposed models designed to mitigate over-smoothing and over-squashing: Multi-Track GCN (MTGCN) (Pei et al., 2024) and Cooperative GNN (CO-GNN) (Finkelshtein et al., 2024). All models are evaluated on Peptides-func and Peptides-struct using consistent training protocols and hyperparameter search spaces.
For CO-GNN, we tune the pesssage-passing scheme (SUMGNN, MEANGNN, GCN, GIN, GAT) as recommended by the original paper. For MTGCN, we optimize the number of message- stages (from to 4). Results are shown in
19

---

Can Classic GNNs Be Strong Basclines for Graph-level Tasks?
Table 17. GCN+ consistently outperforms both models both datasets, confirming its effectiveness in addressing these limitations while retaining architectural simplicity.
Table 17. Comparison with models addressing over smoothng and over-squashing
Peptides-func
Peptides-struct
MTGCN CO-GNN
0.6936 0.0899
0.2461 1: 00019
0.7012 00106
0.2503 # 00025
GCN
0.7261 00067
0.2421 00016
B.4. Visualization
To gain further insights into the representational quality of GNN we visualize the learned graph embeddings using t-SNE. Specifically, we compare embeddings generated by GCN and GCN Peptides-fum ffunde.
Figure shows the resulting t-SNE projections. We observe that the embeddings produced by GCN+ exhibit more distinct class separation and greater inter-class margins compared to those produced by the vanilla GCN.
1-SNE Visualization of GCN Embeddings
L-SNE Visuelization of GCN+ Embeddings
Comprent
-10
35
Component:
Companent
Figure 5. t-SNE visualization of graph-level embeddings learned by GCN (left) and GCN+ (right). Colors denote different classes.
B.5. Static Attention Problem of GTs
To gain insight into why GNNs can outperform GTs in graph-level tasks, analyze the attention patterns in several representative GT architectures. We identified a potential limitation, which we call the static attention problem: the attention scores appear to be globally consistent across all nodes in the graph, irrespective of the query node.
As a concrete example, we analyze GraphGPS on a misclassified molecule from the ZINC dataset (Figure 6). As illustrated, nearly all nodes primarily focus on two structures: the five-membered pyrazoline ring in the top right and the benzene ring the bottom. In contrast, functionally significant substructures, such the C-O-N=N group in the top left and the nitro group (-NO2) in the bottom left, receive minimal attention. This query-invariant attention pattern results in insufficient sensitivity to subgraph structures, which adversely affects prediction accuracy.
In contrast, message-passing GNNs perform node-specific aggregation, allowing the model to capture diverse local substructures more effectively. This beneficial for graph prediction tasks, where node representations are aggregated (e.g.. via global pooling) into a global graph embedding. When nodes encode meaningful subgraph patterns, the resulting graph representation becomes more informative and discriminative.
20

---

Can Classic GNNs Be Strong Baselines for Graph-level Tasks?
Figure Visualization of GraphGPS attention scores across all nodes within same molecular graph. The left side illustrates the molecular structure. The center column displays the attention scores of node (highlighted with green borders) learned by the classic GraphGPS, while right column showeases the 3-hop neighborhood
node. Notably, majority nodes attend predominantly to two structural motifs: the five-mente meemmeeeeeed ring (pyrazoline, top right) and the benzene ring (bottom). In contrast, functionally significant substructures such as C-O-N=N group (top left) and the nitro group (-NO2, bottom left) lefti-receive minimal attention.
C. Further Discussion
C.1. Clarification on GNN+ and Classic GNNs
GNN enhances classic message-passing GNNs by integrating widely adopted architectural techniques including edge features, normalization, dropout, residual connections, FFN, and positional encoding. These components are not novel or specific to any one architecture; rather, they have been widely used across the literature on classic GNNs:
Residual connections, dropout, and normalization were included in the original GCN (Kipf & Welling, 2017).
Edge feature is already incorporated in the early general MPNN framework (Gilmer et al., 2017), which forms the foundation of many classic GNNs such as GatedGCN (Bresson Laurent, 2017).
The use an MLP/FFN after message passing was introduced in the GIN paper (Xu et al., 2018).
Positional encoding has been employed in prior GNN works (Dwivedi al., 2021).
GNN+ unifies these standard components within consistent framework while preserving the core gesssae-ppsssigg mechanism. Our results show that classic GNNs, when equipped with such enhancements, achieve performance comparable to SOTA models on graph-level tasks. We view GNN+ as practical and faithful extension that unlocks the potential of classic GNNs. In this sense, GNN+ is not a departure from the message-passing paradigm, but rather natural evolution that reinforces its core strengths.
21


Reviews:
[{"review_id": "azG0EfLFiT", "reviewer": "Reviewer_s2AU", "summary": "The paper challenges the prevailing assumption that Graph Transformers are inherently superior to Message-Passing GNNs for graph-level tasks. It introduces GNN+, a framework enhancing three classic GNNs (GCN, GIN, GatedGCN) with six techniques: edge feature integration, normalization, dropout, residual connections, feed-forward networks (FFN), and positional encoding (RWSE). Evaluated across 14 graph-level datasets (GNN Benchmark, LRGB, OGB), GNN+ achieves top-three rankings on all datasets and first place on eight, outperforming SOTA GTs while being more computationally efficient. The results suggest that classic GNNs, when properly enhanced, are highly competitive for graph-level tasks, challenging the necessity of complex GT architectures.", "questions": "", "limitations": "", "rating": 1, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your constructive feedback. We believe there may have been some misinterpretations of our work that could have influenced your assessment. We hope our clarifications encourage you to reassess our work.\n\n**(1) Theoretical Analysis**\n\n> This claim holds limited value as it lacks relevant theoretical analysis and deeper mechanistic insights.\n\nWe’d like to clarify that **our work is an empirical benchmarking study**, akin to previous notable benchmarking research such as [1,2,3,4,5]. Consequently, **theoretical analysis is neither intended nor within the scope of this work**.\n\nFurthermore, we'd like to explain **why the empirical study alone represents a significant contribution** to the community.\n\nIn recent years, GTs have emerged as the leading approach for graph-level tasks, often dominating leaderboards, especially on small molecular graphs. This trend has fostered a growing perception that GTs, due to their global attention mechanisms, are inherently superior to GNNs, leading to the marginalization of GNNs in the field. \n\nHowever, our **comprehensive and fully reproducible experimental results**, as presented in Tables 2-4, provide compelling evidence that classic GNNs, when enhanced with our GNN+ framework, can consistently match or outperform GTs across a diverse suite of graph-level tasks. \n\nThis finding has significant implications:\n\n1. It suggests the **global attention** mechanisms of GTs **may not be as useful as commonly believed**. In fact, our ablation study shows they may degrade performance (see our response to Reviewer TFXX, \"(5) Why Attention Fails\"). \n\n2. It questions the need for complex architectures in graph-level tasks and **could prompt a methodological shift** from overly complex GTs to simpler GNN models, potentially reshaping the field's landscape.\n\n3. It explains why state-of-the-art GTs often incorporate **message-passing mechanisms** into their models, either implicitly or explicitly, due to their **high effectiveness**.\n\nAs noted by Reviewer 5voA, \"*While the paper does not introduce new theoretical innovations, it effectively synthesizes existing research, providing valuable insights and a comprehensive summary of current knowledge in the field.*\"\n\n[1] Pitfalls of Graph Neural Network Evaluation, NeurIPS 2018.\n\n[2] A Critical Look at the Evaluation of GNNs under Heterophily: Are We Really Making Progress, ICLR 2023.\n\n[3] Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking, NeurIPS 2023.\n\n[4] A Fair Comparison of Graph Neural Networks for Graph Classification, ICLR 2020.\n\n[5] Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification, NeurIPS 2024.\n\n**(2) Insight into Why Certain Components Help**\n\n> The study's utility in guiding researchers to design or apply GNNs for graph-level tasks remains constrained.\n\nOur ablation studies (Tables 5 and 6) provide **empirical insights** into the contributions of each component of the proposed GNN+ architecture. For example, normalization has a more substantial impact on larger-scale datasets while being less pronounced on smaller ones; similarly, a very low dropout rate (≤0.2) consistently proves optimal for graph-level tasks. These findings greatly improve the usefulness of our work in helping researchers design and apply GNNs for graph-level problems. While we agree that deeper theoretical exploration would further benefit the community, such analysis lies beyond the scope of this benchmarking study. Nonetheless, **our empirical results lay a solid foundation for future theoretical investigations** into how these architectural components enhance graph-level expressiveness and effectiveness.\n\n**(3) Methodological Novelty**\n\n> The core methodology (GNN+) constitutes a direct extension of Luo et al.'s framework for graph-level tasks.\n\nIt is important to note that GNN+ is **NOT** a trivial extension of Luo et al.'s model, which is intended for node-level tasks. Graph-level tasks, especially those involving small-scale molecular graphs, pose unique challenges. To address these, GNN+ integrates various techniques specifically tailored for graph-level modeling, such as edge feature module, FFNs and PE. The simple yet effective design of GNN+ was **established through extensive experiments conducted over half a year**, representing a new framework in the field. \n\n**(4) Visualization Results**\n\nThank you for the suggestion. Following the advice, we have added additional visual analyses (e.g., t-SNE plots of learned embeddings ([link](https://anonymous.4open.science/r/GNN-3221/tsne.jpg)), sensitivity analysis of network depth ([link](https://anonymous.4open.science/r/GNN-3221/layer.jpg)) and dropout rate ([link](https://anonymous.4open.science/r/GNN-3221/dropout.jpg))). In the t-SNE figure, we observe that the graph embeddings generated by GCN+ exhibit greater inter-class distances compared to those generated by GCN. We have incorporated the results in the revised version.", "claims_and_evidence": "The key claim of this paper is that classic GNNs can match the performance of GTs on graph-level tasks by incorporating certain tricks and conducting meticulous parameter search. This claim is supported by substantial empirical evidence. However, this claim holds limited value as it lacks relevant theoretical analysis and deeper mechanistic insights.", "methods_and_evaluation_criteria": "This paper introduces GNN+, an architecture that augments classic GNNs with edge features, normalization, dropout, residual connections, FFNs, and PE. They evaluated GNN+ on 14 graph-level datasets. The evaluation criteria used make sense. However, the work introduces no novel methodological advancements, and the idea of tuning classic GNNs has already been proposed in prior literature.", "theoretical_claims": "The paper is empirical and does not propose new theoretical claims or proofs.", "experimental_designs_or_analyses": "I have checked the experiment section, which is quite comprehensive. They include 14 datasets spanning regression, classification, and inductive tasks and provide rigorous analysis of each component.", "relation_to_broader_scientific_literature": "The paper directly challenges recent works advocating GTs (e.g., GraphGPS) by showing that classic GNNs remain competitive. It extends findings from Luo et al. (2024) to graph-level tasks.", "essential_references_not_discussed": "Key prior works are appropriately cited.", "other_strengths_and_weaknesses": "This paper comprehensively re-examines the performance of GNNs on graph-level tasks, conducting extensive experiments across datasets of varying scales, which is particularly impressive. However, the work suffers from the following limitations:\n1. The study primarily offers empirical observations rather than a deep mechanistic analysis to establish universal design principles for graph-level GNNs. Consequently, its utility in guiding researchers to design or apply GNNs for graph-level tasks remains constrained.  \n2. The core methodology (GNN+) constitutes a direct extension of Luo et al.'s framework for graph-level tasks, with insufficient technical novelty. Notably, while graph-level tasks fundamentally differ from node-level tasks, the paper fails to elucidate how the introduced tricks (e.g., residual connections, PE) specifically enhance graph-level capabilities, such as improving expressiveness for the graph isomorphism problem.  \n3. Despite reporting exhaustive quantitative results, the absence of visualizations hinders understanding of GNN+'s operational advantages and decision-making patterns.", "other_comments_or_suggestions": "I have no other comments or suggestions.", "questions_for_authors": "Refer to Weaknesses.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3vmMxSXjBi", "reviewer": "Reviewer_5voA", "summary": "This study explores the potential of Graph Neural Networks (GNNs) by enhancing them with the GNN+ framework, which incorporates techniques such as edge feature integration, normalization, and positional encoding. The results show that classic GNNs, enhanced with GNN+, outperform Graph Transformers (GTs) on graph-level tasks, achieving top rankings across 14 datasets. While the paper does not present theoretical innovations, it provides an insightful summary and practical evaluation of existing methods, challenging the notion that complex Graph Transformers are necessary for superior performance.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate the very detailed feedback and your recognition of our contributions! We hope our response below will further enhance your confidence in our work.\n\n**(1) Comparison with SOTA GNNs Addressing Over-smoothing and Over-squashing**\n\n> To enhance your paper's contributions, it is recommended to incorporate and compare your proposed method with state-of-the-art techniques addressing over-smoothing and over-squashing in Graph Neural Networks (GNNs) (you mentioned in the first sentence of the Abstract), such as Multi-Track Message Passing and Cooperative Graph Neural Networks. Conducting comparative experiments with these methods will provide a comprehensive evaluation of your approach's effectiveness and highlight its contributions to the field.\n\nWe appreciate the thoughtful suggestion. Following your recommendation, we conducted additional experiments on the Peptides-func and Peptides-struct datasets, comparing GCN+ with SOTA GNNs including Multi-Track Message Passing (MTGCN) [1] and Cooperative Graph Neural Networks (CO-GNN) [2]. These experiments adhered to the hyperparameter settings described in our paper (Lines 264-273).\n\nFor CO-GNN, we tuned its message-passing mechanisms, including SUMGNNs, MEANGNNs, GCN, GIN, and GAT, as recommended by the original paper. For MTGCN, we optimized the number of message-passing stages (from 1 to 4) to ensure a fair comparison.\n\nThe comparative results in the table below clearly demonstrate the effectiveness of our GNN+ framework in addressing over-smoothing and over-squashing. We have included the results in the revised version.\n\n| Model    | Peptides-func ↑     | Peptides-struct ↓   |\n| -------- | ------------------- | ------------------- |\n| MTGCN    | 0.6936 ± 0.0089     | 0.2461 ± 0.0019     |\n| CO-GNN   | 0.7012 ± 0.0106     | 0.2503 ± 0.0025     |\n| **GCN+** | **0.7261 ± 0.0067** | **0.2421 ± 0.0016** |\n\n**(2) The Impact of the Number of Network Layers on GNN+**\n\n> Additionally, performing experiments that involve increasing the number of network layers to observe performance changes can help assess and address the over-smoothing issue in GNNs.\n\nThank you for the thoughtful suggestion. In response, we conducted additional experiments to examine the impact of the number of network layers on GNN+ for the PATTERN, CLUSTER, and PascalVOC-SP datasets. The detailed results can be found at this [link](https://anonymous.4open.science/r/GNN-3221/layer.jpg).\n\nThanks to residual connections, GNN+ achieves optimal performance across a wide range of layers. Specifically, on PATTERN, both GNN+ variants (GCN+ and GatedGCN+) achieve optimal performance at 12 layers. In contrast, on CLUSTER and PascalVOC-SP, their performance continues to improve as the number of layers increases.\n\nOverall, **GNN+ maintains strong predictive performance even at greater depths**, demonstrating its ability to effectively mitigate the over-smoothing issue commonly observed in GNNs.\n\n**(3) Why Not Consider Graph Node Classification**\n\n> Why doesn't the author focus on graph node classification instead of graph classification?\n\nThank you for your question. Our work is inspired by a recent study [3] that showed classic GNNs can achieve performance comparable to, or even surpassing, state-of-the-art GTs for node-level tasks, such as node classification. However, there has been no similar conclusion or investigation for graph-level tasks, and our work aims to fill this gap.\n\nPlease note that we have addressed inductive node classification, which is considered one of the graph-level tasks. Specifically, Table 2 and 3 (in our original manuscript) include results on the PATTERN, CLUSTER, PascalVOC-SP, and COCO-SP datasets, which evaluate the performance of inductive node classification.\n\nIn addition, although our GNN+ framework is specifically designed for graph-level tasks, it can also be applied to node-level tasks. Below are the node classification results on four datasets from CO-GNN: roman-empire, amazon-ratings, minesweeper, and questions. The results indicate that GCN+ consistently achieves performance comparable to CO-GNN.\n\n| Model  | roman-empire | amazon-ratings | minesweeper  | questions    |\n| ------ | ------------ | --------------- | ------------ | ------------ |\n|        | Accuracy↑    | Accuracy↑       | AUROC↑       | AUROC↑       |\n| GCN    | 73.69 ± 0.74 | 48.70 ± 0.63    | 89.75 ± 0.52 | 76.09 ± 1.27 |\n| CO-GNN | 91.57 ± 0.32 | 54.17 ± 0.37    | 97.31 ± 0.41 | 80.02 ± 0.86 |\n| GCN+   | 91.27 ± 0.20 | 53.80 ± 0.60    | 97.86 ± 0.24 | 79.02 ± 0.60 |\n\n---\n[1] Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing, ICML 2024.\n\n[2] Cooperative Graph Neural Networks, ICML 2024.\n\n[3] Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification, NeurIPS 2024.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "Yes", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "This study challenges the prevailing belief that complex Graph Transformer (GT) mechanisms are essential for superior graph-level performance by demonstrating that enhanced classic Graph Neural Networks (GNNs), utilizing the GNN+ framework, can achieve top rankings across multiple datasets.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "While the paper does not introduce new theoretical innovations, it effectively synthesizes existing research, providing valuable insights and a comprehensive summary of current knowledge in the field.", "other_comments_or_suggestions": "1. To enhance your paper's contributions, it is recommended to incorporate and compare your proposed method with state-of-the-art techniques addressing over-smoothing and over-squashing in Graph Neural Networks (GNNs) (you mentioned in the first sentence of the Abstract), such as Multi-Track Message Passing and Cooperative Graph Neural Networks. Conducting comparative experiments with these methods will provide a comprehensive evaluation of your approach's effectiveness and highlight its contributions to the field.\n\n2. Additionally, performing experiments that involve increasing the number of network layers to observe performance changes can help assess and address the over-smoothing issue in GNNs.", "questions_for_authors": "Why doesn't the author focus on graph node classification instead of graph classification?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Gbk2rlmMFA", "reviewer": "Reviewer_TFXX", "summary": "This paper explores techniques inspired by Graph Transformers (GTs) to enhance Graph Neural Networks (GNNs). The authors demonstrate that these enhanced GNNs outperform most GTs on graph-level benchmarks, which contrasts with previous findings in the literature. Additionally, they provide empirical insights into the specific types of graphs on which each technique is most effective.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for recognizing the depth of our experiments and insights. We believe some key points may have been missed and hope our clarification encourages you to revisit and re-evaluate our work.\n\n**(1) Deeper Theoretical Analysis**\n\nThank you for your valuable comment. Please refer to our response to Reviewer s2AU, \"(1) Theoretical Analysis\".\n\n**(2) Fair Comparison between GTs and GNNs**\n\nWe'd like to clarify that our performance comparison is **fair**. \n\nFirstly, the techniques used for GNN+, regarded as hyperparameters—including edge feature module, normalization, dropout, residual connections, FFNs, and positional encoding—have already been incorporated into all GT baselines. Importantly, we retrained the GT baselines using **the same hyperparameter search space as the classic GNNs** (lines 251–255). Therefore, the application of these techniques is **consistent** across both GNN+ and GT baselines.\n\nSecondly, to comprehensively address your concern, we conducted additional experiments utilizing **a fixed model with all six techniques integrated**, denoted as **GNN+ (fixed)**, as presented in the table below. The results show that the fixed GNN+ model achieves performance comparable to the best configurations reported in the paper, demonstrating its practicality and robustness.\n\n|Model|PascalVOC-SP↑|COCO-SP↑|MalNet-Tiny↑|ogbg-molpcba↑|ogbg-code2↑|MNIST↑|CIFAR10↑|PATTERN↑|CLUSTER↑|\n|-|-|-|-|-|-|-|-|-|-|\n|**GCN+**|0.3357±0.0087|0.2733±0.0041|0.9354±0.0045|0.2721±0.0046|0.1787±0.0026|98.382±0.095|69.824±0.413|87.021±0.095|77.109±0.872|\n|**GCN+ (fixed)**|0.3341±0.0055|0.2716±0.0034|0.9235±0.0060|0.2694±0.0059|0.1784±0.0029|98.257±0.063|69.436±0.265|87.021±0.095|76.352±0.757|\n|**GatedGCN**|0.4263±0.0057|0.3802±0.0015|0.9460±0.0057|0.2981±0.0024|0.1896±0.0024|98.712±0.137|77.218±0.381|87.029±0.037|79.128±0.235|\n|**GatedGCN (fixed)**|0.4204±0.0061|0.3774±0.0028|0.9450±0.0045|0.2981±0.0024|0.1889±0.0018|98.712±0.137|77.218±0.381|87.029±0.037|79.128±0.235|\n\n**(3) Justification for Architectural Choices**\n\n> There are multiple ways to combine six techniques through operations like reordering, yet the GNN+ formulation is fixed as in Equation 11. \n\nIndeed, the GNN+ framework **is not limited to the formulation shown in Equation 11**. Instead, it represents a flexible GNN architecture that integrates edge features, normalization, dropout, residual connections, FFNs, and positional encoding, which collectively define GNN+. Each component allows various implementation choices; for instance, both BN and LN are viable options for normalization.\n\nThe specific combination presented in Equation 11 was chosen because our experiments consistently showed strong and, in some cases, remarkable performance. However, our intention was not to advocate exclusively for this configuration. Rather, our primary goal was to clearly demonstrate, through systematic empirical benchmarking, that an enhanced GNN architecture can match or surpass GTs across diverse datasets. Future research can explore alternative architectures and refinements within this flexible framework.\n\n**(4) Insights into How to Enhance GNNs for Graph-Level Tasks**\n\nOur ablation studies (Tables 5 and 6) provide detailed, actionable insights. For example, normalization substantially impacts larger-scale datasets but has less effect on smaller datasets, and a low dropout rate (≤0.2) consistently proves optimal. These findings serve as practical recommendations for researchers seeking to enhance GNN performance.\n\n**(5) Why Attention Fails**\n\nThank you for the thought-provoking question. This phenomenon is the core finding of our study. While GTs employ the global attention mechanism, our results suggest that **it does not benefit graph-level learning as expected** and may even degrade performance by introducing unnecessary complexity.\n\nTo investigate this, we visualized the attention scores of GraphGPS on the Peptides-func dataset (see [this link](https://anonymous.4open.science/r/GNN-3221/attention.jpg)). The visualization shows that the nodes in question (highlighted with green borders) predominantly attend to only one or a few distant nodes, or randomly attend to multiple distant nodes without clear, explainable patterns. \n\nAn ablation study is presented in the table below. The results clearly indicate that the global attention mechanism used in GraphGPS may negatively impact performance. We hypothesize that the global attention mechanism conflicts with the local message-passing mechanism of GNNs, causing the model to excessively attend to distant, less relevant nodes. This phenomenon, referred to as the **over-globalizing problem**, has also been experimentally validated in recent studies [1].\n\n|Model|Peptides-func↑|\n|-|-|\n|**GraphGPS (GNN + Attention)**|0.6534 ± 0.0091|\n|**GraphGPS (GNN only, w/o Attention)**|0.6951 ± 0.0134|\n|**GraphGPS (Attention only, w/o GNN)**|0.6366 ± 0.0163|\n\n[1] Less is More: On the Over-Globalizing Problem in Graph Transformer, ICML 2024.", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "See **Other Strengths And Weaknesses**.", "theoretical_claims": "No proofs to be checked.", "experimental_designs_or_analyses": "Yes.", "relation_to_broader_scientific_literature": "The findings prompt a reevaluation of whether Graph Transformers are truly necessary, given their complexity, especially considering that enhanced GNNs can achieve superior performance on graph-level tasks.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "**Strengths.** The experiment is comprehensive with respect to benchmarks and baselines. It effectively validates the argument that \"GNNs with enhancements can surpass Graph Transformers on graph-level tasks,\" providing valuable insights for the literature.\n\n**Weakness.**  \n-  The paper dedicates considerable space to experimental settings, results, and observations, but it lacks **deeper analysis**, which would offer more valuable and insightful contributions. The proposed method feels more like a successful technical trial. Notably, I’m not dismissing technical works; my main concern is that the finding—'GNNs with enhancements can surpass Graph Transformers on graph-level tasks'—**alone** is not enough to support a strong paper.\n- I notice that the six techniques are selectively applied, as shown in Table 6. This implies that the **GNN+ architectures vary across benchmarks, with only the best-performing configurations being reported from Table 2 to Table 4**. In contrast, the architecture of the GT baselines remains consistent across benchmarks. This discrepancy introduces a degree of unfairness and weakens the argument that 'GNNs perform better than GT.' Additionally, the GNN+ architecture appears to be less practical due to the heavy dependence on the dataset.", "other_comments_or_suggestions": "In the ablation study section, I recommend emphasizing the analysis to ensure it isn't overshadowed by the description of the phenomena.", "questions_for_authors": "1. For the six techniques explored, there are **multiple ways to combine them** through operations like reordering, yet the GNN+ formulation is fixed as in Equation 11.  Other similar confusion occurs—why choose BN over LN, and why add edge features in the chosen way? ... Do other variants perform similarly well or worse. If they perform worse, are there any explanations? Additionally, it would be valuable to provide insights into **how we should enhance GNNs for graph-level tasks**.\n\n\n2. I note that GTs also incorporate all the techniques including edge feature integration, normalization, dropout, residual connections, feed-forward networks, and positional encoding. **It seems that the attention machenism may not offer benefits, and may even hurt performance compared to message-passing.** Are there any explanations for this observation?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NGdU0Yuv8N", "reviewer": "Reviewer_FRSj", "summary": "The paper presents GNN+, a framework that enhances Graph Neural Networks (GNNs) using six components—edge features, normalization, dropout, residual connections, feed-forward networks (FFNs), and positional encoding—to address issues such as over-smoothing and capturing long-range dependencies.\n\nThrough benchmark evaluations, GNN+ models demonstrate superior performance and efficiency compared to Graph Transformers (GTs), often securing top positions. \n\nEach component of GNN+ significantly enhances the capability of GNNs, making them a simpler yet competitive option for graph-level tasks.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thorough review and for acknowledging our contributions. We sincerely hope our response below further strengthens your confidence in our work.\n\n**(1) Related Works**\n\nThank you for sharing these related works. We have integrated detailed discussions of these relevant studies ([2], [3], [4], and others) into the revised manuscript to better contextualize our contributions. \n\n**(2) Performance vs. Efficiency Trade-off Analysis of Fast Baseline Models and GNN+**\n\nThank you for your thoughtful question. The GT baselines used global attention mechanisms with *quadratic* computational complexity, resulting in higher computational costs without performance gains compared to GNNs. To address your question, we conducted additional experiments using two *linear* baseline models—HRN [3] and LDP [4]—and compared their performance with GNN+.\n\nFirst, we evaluated these models on five **small-scale** graph classification datasets (IMDB-B, IMDB-M, COLLAB, MUTAG, and PTC) used in [3, 4]. These datasets consist of only 300–5000 graphs, substantially smaller than the main datasets (>10,000 graphs) used in our benchmarking study. Under identical experimental setups from their papers, GCN+ achieved comparable performance to these fast baselines. Given the small size of the datasets, their computation times are equally fast.\n\n|Model (Accuracy)|IMDB-B↑|IMDB-M↑|COLLAB↑|MUTAG↑|PTC↑|\n|-|-|-|-|-|-|\n|\\# graphs|1000|1500|5000|188|344|\n|LDP|75.4|50.0|78.1|90.3|64.5|\n|HRN|77.5|52.8|81.8|90.4|65.7|\n|GCN+|76.9|52.3|80.9|90.1|66.6|\n\nFurther, we extended this comparison to **large-scale** OGB datasets (ogbg-molhiv and ogbg-molpcba) using identical experimental setups. Although LDP and HRN showed faster training times than GCN+, their performance was significantly lower. This highlights the importance of rigorous benchmarking on larger scales. Evaluating models solely on small datasets, like MUTAG and PTC, can mask true differences in generalization capabilities. Results from large-scale datasets clearly demonstrate GCN+'s superior predictive performance, validating our focus on comprehensive and systematic benchmarking.\n\n|Model|ogbg-molhiv (41,127 graphs)||ogbg-molpcba (437,929 graphs)||  \n|-|-|-|-|-|  \n||AUROC↑|Training Time (epoch/s)↓|Avg. Precision↑|Training Time (epoch/s)↓|  \n|LDP|0.7121±0.0105|5s|0.1243±0.0031|31s|  \n|HRN|0.7587±0.0147|9s|0.2274±0.0043|68s|  \n|GCN+|0.8012±0.0124|16s|0.2721±0.0046|91s|\n\nThank you once again for helping us improve our work. These results have been included in the revised version of our manuscript.\n\n**(3) Future Theoretical Research**\n\nWe appreciate your comment and suggestion. We have incorporated a discussion of **limitations and future research directions** into the revised manuscript. \n\nOur work is an **empirical benchmarking study** (please see our reply to Reviewer s2AU \"(1) Theoretical Analysis\"). While deeper theoretical exploration would be valuable, such analysis lies **beyond the scope of this benchmarking study**.\n\nAs noted by Reviewer 5voA, \"*While the paper does not introduce new theoretical innovations, it effectively synthesizes existing research, providing valuable insights and a comprehensive summary of current knowledge in the field.*\"\n\nThe **empirical insights** derived from this work, particularly from the ablation studies (Tables 5 and 6), offer **valuable guidelines** for researchers in designing and applying GNNs to graph-level problems. These findings also **lay a solid foundation for future theoretical investigations**.\n\n**(4) Hyperparameter Tuning and Fairness of Comparison between GTs and GNNs**\n\nThank you for highlighting this concern. We applied equally thorough hyperparameter tuning to all GT baselines, incorporating edge features, normalization, dropout, residual connections, FFNs, and positional encoding. All models were retrained using the same hyperparameter search space as GNN+ (lines 251–255).\n\nNotably, GNN+ remains robust and dataset-agnostic. Please see our response to TFXX, \"(2) Fair Comparison between GTs and GNNs\".\n\n**(5) Hyperparameter Sensitivity Analysis**\n\nThank you for your thoughtful suggestion. In response, we conducted additional experiments to systematically evaluate the sensitivity of GNN+ performance to critical hyperparameters, specifically dropout rates ([link](https://anonymous.4open.science/r/GNN-3221/dropout.jpg)) and the number of layers ([link](https://anonymous.4open.science/r/GNN-3221/layer.jpg)).\n\n- **Dropout Rates:** Our results indicate that a low dropout rate (≤ 0.2) is sufficient and optimal, whereas higher dropout rates significantly degrade performance.\n- **Number of Layers:** Residual connections enable GNN+ to achieve optimal performance across a wide range. Increasing the number of layers does not lead to sudden performance drops, indicating effective mitigation of the over-smoothing problem.\n\nFor other hyperparameters that are binary (enabled or disabled), please refer to our ablation studies in Tables 5 and 6.", "claims_and_evidence": "The claims are supported by empirical evidence, comparing GNN+ with state-of-the-art models across 14 datasets, as detailed in Tables 2, 3, and 4.\n\nAdditionally, thorough ablation studies isolate and evaluate the contributions of each component within the GNN+ framework in Tables 5 and 6.\n\nHowever, the enhanced performance of GNN+ might depend heavily on meticulous hyperparameter tuning, suggesting that improvements might stem more from this tuning rather than the architecture's inherent superiority.", "methods_and_evaluation_criteria": "The proposed methods and evaluation criteria are well aligned with the problem of improving graph-level performance. \n\nBy enhancing classic GNNs with six components, the approach directly addresses known limitations of traditional GNNs. \n\nMoreover, the choice of benchmark datasets—including those from the GNN Benchmark, Long-Range Graph Benchmark (LRGB), and Open Graph Benchmark (OGB)—is appropriate because they cover diverse applications widely recognized in the community.", "theoretical_claims": "The submission does not include any formal theoretical proofs for its claims. \n\nInstead, it presents standard equations that define classic GNN operations (such as those for GCN, GIN, and GatedGCN) along with modifications that integrate components such as edge feature integration, normalization, dropout, residual connections, FFN, and positional encoding. \n\nThese equations are largely descriptive and serve to illustrate how the GNN+ framework is constructed rather than providing rigorous proofs of new theoretical properties.", "experimental_designs_or_analyses": "The authors evaluated GNN+ across 14 benchmark datasets from three prominent sources (GNN Benchmark, LRGB, and OGB), reporting mean performance and standard deviations over five runs.\n\nMoreover, the design includes comprehensive ablation studies in Section 5.2 that systematically remove individual components to isolate their contributions.\n\nThe hyperparameters used are reported in the Appendix Section A.3.", "relation_to_broader_scientific_literature": "The key idea of the paper is to enhance classic GNNs by systematically integrating six techniques that are themselves well-established in the literature.\n\nFour of these techniques—normalization, dropout, residual connections, and feed-forward networks—are well-recognised in the literature for improving GNN performance, albeit in node classification tasks [1].\n\nThe integration of the remaining two components—edge features and positional encoding—is straightforward, aligning with existing knowledge: edge features are crucial for molecular datasets, and positional encodings are widely used in graph transformers.\n\n[1] Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification, In NeurIPS'24.", "essential_references_not_discussed": "Previous research [2] highlights the necessity of standardizing experimental protocols in evaluating GNNs, as it reveals that many GNN architectures fail to consistently surpass structure-agnostic baselines, especially in chemical datasets. \n\nThis context is significant for GNN+ of this submission, as it aligns with rigorous evaluation standards, ensuring that its performance gains are genuine and robust rather than results of experimental artifacts or overfitting.\n\nA detailed discussion of simple approaches [3, 4] would not only highlight alternative pathways to simplicity and efficiency but also provide valuable insights for further enhancing the design of classic GNNs as pursued in the GNN+ paper.\n\n[2] A Fair Comparison of Graph Neural Networks for Graph Classification, In ICLR'20,\n[3] A Simple yet Effective Method for Graph Classification, In IJCAI'22,\n[4] A simple yet effective baseline for non-attributed graph classification, In ICLR'19 Workshop: Representation Learning on Graphs and Manifolds.", "other_strengths_and_weaknesses": "Strengths:\n\n[+] Besides achieving competitive or superior accuracy compared to state-of-the-art Graph Transformers, the paper demonstrates that the enhanced classic GNNs are computationally more efficient, an important practical advantage.\n\n[+] The paper clearly outlines each component of the proposed method and provides well-structured experimental results. The detailed description of the experimental setup and hyperparameter tuning further enhances transparency and reproducibility.\n\nWeaknesses:\n\n[-] While the paper excels in its empirical contributions, it does not provide new theoretical insights or rigorous proofs to support the improvements.\n\n[-] The performance gains appear to rely on extensive and careful hyperparameter tuning. This raises questions about the robustness of the improvements when applied in different settings or on different types of graphs beyond the benchmark datasets used.", "other_comments_or_suggestions": "The submission would benefit from a discussion on limitations and future research directions. \n\nFor instance, despite the strong empirical results, a detailed theoretical analysis of when and why GNN+ surpasses the performance of Graph Transformers would enhance understanding. \n\nAdditionally, establishing formal bounds on expressiveness and generalization could inform the design of future models.", "questions_for_authors": "1. Was a hyperparameter sensitivity analysis performed for critical hyperparameters in GNN+, and if so, how sensitive is GNN+ to changes in these parameters?\n2. Were all the baseline models, particularly the graph transformer baselines, tuned with the same level of rigor as GNN+?\n3. Were the six components—edge features, normalization, dropout, residual connections, feed-forward networks (FFNs), and positional encoding—carefully integrated into the baseline models and compared with GNN+ models?\n4. Was a trade-off analysis conducted to compare test data performance (e.g., accuracy) and training performance (e.g., training time) between fast baseline models and GNN+ models?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The paper argues that by enhancing them with edge feature integration, normalization, dropout, residual connections, feed-forward networks (FFN), and positional encoding, classic (message passing-based) GNNs match or surpass the performances of graph transformers, contrary to the common belief. This hypothesis is tested (and largely confirmed) in 14 datasets and against 21 graph transformer architectures, in what appears to be a fair comparison.

The paper was welcomed with mixed reviews. Most of the concerns can be summarized as follows:
- the paper does not provide theoretical insights
- it does not offer design insights
- the finding is over-generalized

Personally, all these concerns appear inconsistent, especially after the rebuttal. 

- First of all, this is a benchmark paper whose focus is not on theory. Clearly, theory would add further value to this paper; however, theoretical insights were not central in studies which share the same objective as this [1-4]. In this sense, I **expect** the authors to emphasize the empirical nature of the insights offered by this paper in their camera-ready version, and to explicitly acknowledge the lack of a theoretical analysis as one of its current limitations (which should be addressed by subsequent works).
- The paper **does** offer design insights: arguably, the core contribution of the paper is a specific design which makes classic message-passing architectures compete with graph transformers, while being more efficient. Its primary contribution is indeed a way to design a GNN architecture which empirically matches or surpasses GTs' performances, where the importance of each component to the final architecture is tested via ablation studies. 
- The main finding is shown on three different GNN architectures compared to a whole lot of graph transformers in a wide range of datasets. Given the width and depth of the analysis, I see a clear trend rather than an over-generalization. 

Overall, the findings in this article are also valuable for GNN practitioners, which could, e.g., decide to resort the proposed architecture rather than GTs when efficiency is critical.

In my opinion, the graph learning community has only to benefit from these benchmarking analyses that test very precise and significant hypotheses, and do so extensively and fairly.

My recommendation is to accept this paper.

References:

[1] Shchur et al. Pitfalls of graph neural network evaluation. Neurips R2L 2018

[2] Errica et al. A fair comparison of graph neural networks for graph classification. ICLR 2020

[3] Lv et al. Are we really making much progress?: Revisiting, benchmarking and refining heterogeneous graph neural networks. KDD 2021

[4] Luo et al. Classic GNNs are strong baselines: Reassessing GNNs for node classification. Neurips 2024
Decision: Accept (poster)