Graph homophily booster: Reimagining the role of discrete features in heterophilic graph learning
Authors: Ruizhong Qiu
Ting-Wei Li
Gaotang Li
Hanghang Tong
Venue: ICLR 2026 Poster (2026)
Keywords: Graph Machine Learning
Graph Neural Network
Heterophily
Primary area: learning on graphs and other geometries & topologies

Abstract: Graph neural networks (GNNs) have emerged as a powerful tool for modeling graph-structured data, demonstrating remarkable success in many real-world applications such as complex biological network analysis, neuroscientific analysis, and social network analysis. However, existing GNNs often struggle with heterophilic graphs, where connected nodes tend to have dissimilar features or labels. While numerous methods have been proposed to address this challenge, they primarily focus on architectural designs without directly targeting the root cause of the heterophily problem. These approaches still perform even worse than the simplest MLPs on challenging heterophilic datasets. For instance, our experiments show that 23 latest GNNs still fall behind the MLP on the Actor dataset. This critical challenge calls for an innovative approach to addressing graph heterophily beyond architectural designs. To bridge this gap, we propose and study a new and unexplored paradigm: directly increasing the graph homophily via a carefully designed graph transformation. In this work, we present a simple yet effective framework called Graph Homophily Booster (GRAPHITE) to address graph heterophily. To the best of our knowledge, this work is the first method that explicitly transforms the graph to directly improve the graph homophily. Stemmed from the exact definition of homophily, our proposed GRAPHITE creates feature nodes to facilitate homophilic message passing between nodes that share similar features. Furthermore, we both theoretically and empirically show that our proposed GRAPHITE significantly increases the homophily of originally heterophilic graphs, with only a slight increase in the graph size. Extensive experiments on challenging datasets demonstrate that our proposed GRAPHITE significantly outperforms state-of-the-art methods on heterophilic graphs while achieving comparable accuracy with state-of-the-art methods on homophilic graphs. Furthermore, our proposed graph transformation alone can already enhance the performance of homophilic GNNs on heterophilic graphs, even though they were not originally designed for heterophilic graphs. Our code is publicly available at https://github.com/q-rz/ICLR26-GRAPHITE .

Published as a conference paper at ICLR 2026
GRAPH HOMOPHILY BOOSTER: REIMAGINING THE ROLE OF DISCRETE FEATURES IN HETEROPHILIC
GRAPH LEARNING
Ruizhong Qiu; Ting-Wei Li; Gaotang Li, Hanghang Tong University of Illinois Urbana- Draama-hamppagn. IL. USA {rq5, twli, gaotang3. htong] @ illinois. edu
ABSTRACT
Graph neural networks (GNNs) have emerged as powerful approach to modeling graph-structured data and demonstrated remarkable success many real-world applications such as complex biological network analysis, neuroscientific anal- ysis, and social network analysis. However, existing GNNs often struggle with heterophilic graphs, where connected nodes tend to have dissimilar features labels. While numerous methods have been proposed to address this challenge, they primarily focus architectural designs without directly targeting the root cause of the heterophily problem. These approaches still perform even worse than the simplest multi Multi--lyer perceptrons (MLPs) challenging heterophilic datasets. For instance, our experiments show that 23 latest GNNs still fall behind the MLP the ACTOR dataset. This critical challenge calls for an innovative approach to addressing graph heterophily beyond architectural designs. To bridge this gap. we propose and study a new and unexplored paradigm: directly increasing the graph homophily via carefully designed graph transformation. In this work, we present a simple yet effective framework called GRAph homoPHIly boosTEr (GRAPHITE) to address graph heterophily. To the best of our knowledge, this work is the first method that explicitly transforms the graph to directly improve the graph homophily. Stemmed from the exact definition of homophily, our pro- posed GRAPHITE creates feature nodes to facilitate homophilic message passing between nodes that share similar features. Furthermore, and empirically show that our proposed GRAPHITE significantly increases the homophily of originally heterophilic graphs, with only a slight increase in the graph size. Extensive experiments on challenging datasets demonstrate that our proposed GRAPHITE significantly outperforms state-of-the-ar methods on het- erophilic graphs while achieving comparable accuracy with state-of-the-art meth- ods on homophilic graphs. Furthermore. our proposed graph transformation alone can already enhance the performance of homophilic GNNs on heterophilic graphs, even though they were not originally designed for heterophilic graphs. Our code publicly available at itttt::////// / thub. com/ q- rz. CCL22 6- GRAPHITE
both theoretically
INTRODUCTION
Graph neural networks (GNNs) have emerged as a powerful class of models for learning on topo- logically structured data. Their ability to incorporate graph topology and node-level attributes has enabled them to achieve state-of-the-art performance in a wide range of applications. These include protein function prediction, where GNNs model complex biological networks (You et al. 202T
Réau et al. 2023); neuroscientific analysis, where they used to model brain networks Li al.
2023a); and social network analysis, where they help uncover patterns among users (Li et al. 2023c
A critical challenge that many GNNs are faced with is that real-world networks can exhibit het- erophily, where connected nodes tend to have dissimilar features or labels. Examples include protein interaction networks where different types of proteins interact (Zhu et al.] 2020
"Equal contribution.

---

Published as a conference paper at ICLR 2026
Original Heterophilic Graph
More Homophilic GRAPHITE
17g
GRAPHITE (Ours)
X
X2
V2
2/2
X
Original Graph Nodes
Artded Fecture Nodes
Graph Node Features
Original Graph Foges
Addied Fasture Fdgos
Figure 1: Illustration of our proposed GRAPHITE. Feature nodes/edges facilitate more homophilic message passing. For instance, feature node # facilitates homophilic message passing between nodes v U22 and feature node z2 facilitates homophilic message passing among nodes v3, 04, VG-
online marketplace networks where buyers connect with sellers rather than other buyers Pann- dit et al. 2007). Standard GNN architectures (Kipf' & Welling. 2016)] Wu et al. 2019 Veliškovid al.) 2019), with their heavy reliance on neighborhood aggregation, often struggle with heterophilous graphs since aggregating features from dissimilar neighbors can dilute or distort node representations. Existing methods for heterophilic graphs mainly focus on designing new GNN architectures as workarounds for het- erophilic graphs, such separating ego and neighbor embeddings (Zhu al..
al. 2017; |aamiloon al., 2017 Chen et al. [ Abu-El-Haija
2020 Zhang et al. 2023), incorporating multi-hop information via learnable weights (Chien et al. [2020] Dong et al.,
2024., , and adaptive self-gating to leverage both low- and high-frequency signals (Bo et al. 2021)
More recent solutions introduce frequency-ban based filtering to handle both homophily and heterophily
or leverage adaptive residual connections to further enhance ficxibility (Xu ct al.) 2023 2024a
Yan
ct al. 2024).
Despite plenty of architectural advances, many GNNs still perform even worse than the simplest multi-layer perceptrons (MLPs) challenging heterophilic graphs. For instance, Table 2 shows that 23 latest GNNs still fall behind the MLP on the ACTOR dataset. This critical challenge calls for an innovative approach to addressing graph heterophily beyond architectural designs.
To bridge this gap, we propose and study a new and unexplored paradigm: directly increasing the graph homophily via carefully designed graph transformation. In this work, we present simple yet effective framework called GRAph homoPHily boosTEr (GRAPHITE) to address graph heterophily. To the best of our knowledge, this work is the first method that explicitly transforms the graph to directly improve the graph homophily.
Our key idea is rooted in the exact definition of homophityyheterophiiy, In homophilichhettrophiiic graph, nodes that share similar features are more/less likely to be adjacent, respectively. Therefore, natural idea increase the graph homophily similar features so as to facilitate homophilic message passing. However, naïvely adding mutual connections between such node pairs can drastically increase the number of edges. To reduce the number of "shortcut" edges, we propose to connect such node pairs indirectly instead. In particular, we introduce prroducefeeturee nodes as "hubs" and connect graph nodes to their corresponding feature nodes. We further theoretically show that our proposed method can provably enhance the homophily of originally heterophilic graphs without increasing the graph size much.
create "shortcut" connections between nodes with
Our main contributions are summarized as follows:
New paradigm. We propose and study a new and unexplored paradigm: provably increas- ing the graph homophily directly via non-learning- based graph transformation. This paper is the first work on this paradigm to the best of our knowledge.
Proposed method. We propose simple yet effective method called GRAPHITE. which creates feature nodes as "shortcuts" to facilitate homophilic message passing between nodes with similar features.
2

---

Published as a conference paper at ICLR 2026
Theoretical guarantees. We theoretically show that GRAPHITE can provably enhance the homophily of originally heterophilic graphs with only a slight increase in size.
Empirical performance. Extensive experiments on challenging datasets demonstrate the effectiveness of our proposed GRAPHITE. GRAPHITE significantly outperforms state-of- the-art methods on heterophilic graphs while achieving comparable accuracy with state- of-the-art methods on homophilic graphs. Furthermore, our proposed graph transformation alone can already enhance the performance of homophilic GNNs on heterophilic graphs.
PRELIMINARIES
2.1 NOTATION
An undirected graph with discrete node features can be represented as a triple G = (V,E, X). where
(o1....,.Ip) denotes the node set, S V x denotes the edge set, X {0, 1] is binary node feature matrix representing discrete node features, and X' {l...., | } is the feature set containing all the discrete node features. In addition to that, each graph node eú E V has node
label ye. V where Y the label set with | classes.
2.2 PROBLEM DEFINITION
In this paper, study two key problems: (i) how to transform a graph to increase its homophily and (ii) how to perform node classification on a heterophilic graph datasets. Formally, we introduce the problem definitions follows.
Problem (boosting graph homophily). Given a highly heterophilic graph, transform the graph to increase its homophily. Input: a heterophilic graph G. Output: transformed graph G* with higher homophily.
Problem 2 (semi-supervised node classification on a heterophilic graph). Given a heterophilic graph and a set of labelled nodes, train a model to predict the labels of unlabelled nodes. In- put: (i) a heterophilic graph G (V, X)); (ii) a labelled node set VL V whose node labels [eoeeren, are available. Output: the predicted labels of unlabeled nodes V V2.
3 PROPOSED METHOD: GRAPHITE
In this section, we propose a simple yet effective graph transformation method called GRAph homoPHilly boosTEr (GRAPHITE) that efficiently increase the homophily of a graph. In Sec- tion 3.1 we will introduce the motivation our proposed GRAPHITE. First, we will present the design of our proposed method GRAPHITE. Then, we will describe the neural architecture of proposed method. Due to the page limit, proofs of theoretical results are deferred to the appendix.
3.1 MOTIVATION: NAIVE HOMOPHILY BOOSTER
Graph heterophily ubiquitous challenge in graph-based machine learning. On a highly het- erophilic graph, many neighboring nodes exhibit dissimilar features or belong to different classes. As result, graph heterophily limits the effectiveness of GNN message passing, as standard aggre- gation schemes might fail to capture meaningful patterns in heterophilic neighbors.
Existing methods for heterophilic graphs mainly focus on designing workarounds such as new archi- tectures or learning paradigms for heterophilic graphs, including adaptive message passing, higher- order neighborhoods, or alternative propagation mechanisms that leverage both local and global graph structures.
In contrast to existing workaround methods, we propose a new method that aims to directly increase the homophily of the graph via specially designed graph transformation. To the best of our knowl- edge, this work is the first method that explicitly transforms the graph to improve the homophily of the graph.
Our idea is rooted in the exact definition of homophily and heterophily. In a heterophilic graph, nodes that share similar features more likely be non-adjacent. However, in homophilic
3

---

Published as a conference paper at ICLR 2026
Feature Homophily
BB Original
GRAPHITE
Adjusted Homophily
0.15
0.4
0.3
8.30
0.1
0.0
0.00
Actor
Squlmrl-F
Chameleon--
Minesweoper
Actor
Sculmot-F
Chamelcon-
Minesweeper
Figure 2: Our proposed GRAPHITE significantly increases the homophily of originally heterophilic graphs. We report two latest homophily metrics: feature homophily (Jin et al. 2022) and adjusted homophity (Platonov al. 2024).
graph, nodes that share similar features should be more likely to be neighbors. Therefore, a natural idea to increase the homophily of the graph is to create "shortcut" connections between nodes with similar features, which will facilitate homophilic message passing between them.
Before introduce the proposed method, let's consider the following naive approach imple- menting the aforementioned idea: For each pair of nodes 04, E V, if they share at least a feature (i.e.,) X [u, :] J (A [0y, =] > 0), add "shorteut" edge (U;, 0ÿ) between them. Let's call this ap- proach the naive homophily booster (NHB). The following Theorem shows that NHB can indeed increase the homophily of the graph under mild and realistic assumptions.
Theorem 1 (naive homophily booster). Given heterophilic graph = (V, V,E,X) X),, let El denote the set of edges after adding the NHB "shortcut" edges, and let gt == (V, X) denote the graph transformed by NHB. Under mild and realistic assumptions in Appendix D.I] we have
hom(g") hom(g),
1 18 < O(113))
However, Equation 2 also shows that NHB is extremely inefficient despite its effectiveness in increasing homophily. For instance, even the graph has only 2,000 nodes, NHB can add as many as 1.999,000 "shortcut" edges. The plenty of "shortcut" edges can drastically slow down the training and the inference process of GNNs. Hence, this naïve approach computationally impractical for GNNs. To address this computational challenge, will instead propose efficient homophily booster via a more careful design of "shortcut" edges.
3.2 EFFICIENT GRAPH HOMOPHILY BOOSTER
To address the computational inefficiency of the motivating naïve approach above, we propose an efficient, simple yet effective graph transformation method called GRAph homoPHIly boosTEr (GRAPHITE) this subsection.
Note that the large number of NHB "shortcut" edges is because NHB directly connects nodes with similar features. Since there are (((v(2) node pairs a graph, then the total number of added NHB "shortcut" edges can be as large O(D)²).
To reduce the number "shortcut" edges, we propose to connect such node pairs indirectly instead. In particular, if we can create a few auxiliary "hub" nodes so that all such node pairs indirectly connected through the "hub" nodes, then we will be able to significantly reduce the number of "shortcut" edges at only small price of adding few "hub" nodes. Therefore, need to develop an appropriate design of the "hub" nodes.
Graph transformation. Following the aforementioned motivation, we propose to create a feature node I& for each feature serve the "hub" nodes. Let Vx denote the of feature nodes:
Vx = {xx :a:kkkc E
To distinguish feature nodes Vx from nodes in the original graph, we call V graph nodes from now For each graph node E V, if graph node e; has feature k (i.e., X (1s, k) 1), we add an
4

---

Published as a conference paper at ICLR 2026
edge (V,, F2k) connect the graph node and the feature node 24 E Vx, and we call it feature edge. Let Ex denote the set of feature edges:
Ex {(v,, mb): 2s V, 2 E Vx. ( Ab] CVXVx.
To distinguish feature edges εx from the original edges &, we call graph edges from now on.
Finally, we define the transformed graph g (V*,E", X") follows. The nodes V of the transformed graph G are the original graph nodes V and the added feature nodes Vx: V == VUVx. The edges e" of the transformed graph 9 are the original graph edges & and the added feature edges Ex: 8 == & U Ex. We can also equivalently define the edges of the transformed graph G" by its adjacency matrix. Let A denote the adjacency matrix of the original graph G. Then, the adjacency matrix A of the transformed graph G can be expressed in block matrix form:
A X 0
X
A
(4)
It remains to define node features X E XV of the transformed graph. For each graph node V E V, we use its original features as its node features: ["v;; = X [u] For each feature node Ie E Vx, we define its node feature as the average feature vector among the graph nodes ey that connected to feature node z&:
X [ Ex (xx)))
E
X :l
(5)
Our proposed graph transformation GRAPHITE is illustrated in Figure I In this example, {U1, U2 23, U1 U5} are the graph nodes, where V1, U2 belong to one class, and v3 Gg Uy belong to the other class. Our proposed GRAPHITE adds feature nodes 21, 22, 23 to the graph. We can that feature node z facilitates homophilic message passing between 81, U2, and that feature node 22 facilitates homophilic message passing among U3 04, 05.-
Theoretical guarantees. The transformed graph 9 enjoys a few theoretical guarantees. First, an important property of the feature edges is that every pair of nodes that share features can be connected through feature edges within two hops, formally stated in Observation [ This ensures that nodes with similar features are close to each other on the transformed graph g facilitating homophilic message passing.
Observation 2 (two-hop indirect connection). For each pair of nodes u, E V, if they share least a feature (i.e., |[ A [0y, :llloe >> 0), then v, and vj are two-hop neighbors on the transformed graph g*.
Furthermore, we theoretically show that our proposed graph transformation GRAPHITE can in- crease the homophily of the graph without increasing the size of the graph much, as formally stated in Theorem 3
Theorem 3 (efficient homophily booster). Given a heterophilic graph G (V,E,),, let G* := EV*,"*, xx) denote the graph transformed by our proposed GRAPHITE. Under mild and realistic assumptions in Appendix D. we have
hom(g") > hom(g),
(6)
|' (VVV), |8 S O(EE).
(7)
The effectiveness of our proposed GRAPHITE is also empirically validated in Section 4.3 As shown in Figure []] our proposed GRAPHITE significantly increases the homophily of originally heterophilic graph.
3.3 NEURAL ARCHITECTURE
The transformed graph G* can be readily fed into existing GNNs to boost their performance, even when the GNNs were originally designed for homophilic graphs, demonstrated in Table Mean- while, to maximize the GNN performance on the transformed graph G*, we introduce a GNN archi- tecture specially designed for the transformed graph in this subsection.
5

---

Published as a conference paper at ICLR 2026
Table Summary of dataset statistics, We use four heterophilic graphs and homophilic graphs.
Heterophilic Graphs
Homophilic Graphs
Statistic
ACTOR
SQUIRREL-F
CHAMBLEON-F MINESWEEPER
CoRA
CITESEER
# Nodes
7600
2223
890
10000
2708
3327
# Edges
33544
46998
8854
39402
5429
4732
# Features
931
2089
2325
7
1433
3703
# Classes
5
5
5
2
7
6
Homophily
0.0028
0.0086
0.0295
0.0094
0.7711
0.6707
To help the GNN distinguish graph nodes V from feature nodes Vx, for different edges. As a reference weight, suppose that graph edges & have weight Wg := Let wx denote the weight of feature edges Ex. Following GCN (Kipf & Welling (016), also use self-loops in GNN message passing; let wo > 0 denote the weight of selt-loops.
different edge weights
Let du denote the weighted degree of each node V". Specifically, for each graph node it E V,
dy, := wo +
Σ (F.R))EE
we +
Σ
wxx
(8)
and for each feature node Fg E Vx.
de == Wh +
Σ (EE.
WX.
(9)
Inspired by FAGCN (Bo et al., 2021), we use self-gating mechanism in GNN aggregation. For
each node V*, let h. E T denote the embedding of node u before GNN aggregation, where m is the embedding dimensionality. Then, the self self-gating score au, between two nodes u, u' e V
defined as
Oug == tanh (a (aT(h. hww)) + +b).
(10)
T
where || denotes the concatenation operation, a 22m and b IR are learnable parameters, and > 0 is temperature hyperparameter.
Next, we describe our aggregation mechanism. For each node E V", let h2, Em denote the embedding of node u after GNN aggregation. For each graph node E V, we define
WyCKo; hug + Σ
huy + Σ de, ddy, (Bna))EEX dr, dder
Olii.us
wxOn, hzz
(11)
dd, ddu
and for each feature node Iq E Vx,, we define
h
d Pr
(12)
ddn
Vdyy / dry.
(v.
Furthermore, add a multi-layer perceptron (MLP) with residual connections after each GNN aggregation. We use the GELU activation function (Hendrycks Gimpel 2016).
EXPERIMENTS
We conduct extensive experiments on both heterophilic and homophilic datasets answer the fol- lowing research questions:
RQ1: How does our proposed GRAPHITE compare with state-of-the-art methods?
RQ2: How much improvement
GRAPHITE achieve in terms of the graph homophily?
RQ3: Can our graph transformation alone enhance the accuracy of homophilic GNNs?
RQ4: How should we design the features of feature nodes our GRAPHITE?
RQ5: How efficient is our GRAPHITE?
RQ6: How effective is GRAPHITE under various hyperparameters?

---

Published as a conference paper at ICLR 2026
Table Comparison with existing methods. GRAPHITE significantly outperforms sate-of-the-arr methods on heterophilic graphs while achieving comparable accuracy with ofte-offthhearr methods on homophilic graphs. Best results are marked bold, and second best results are underlined.
Heterophilic Graphs
Homophilic Graphs
Method
ACTOR
SQUIRREL-F
CHAMELBON-F
MINESWEEPER
CoRA
CITESEER
ML.P
35.04 + 1.53
33,91 + 1.55
38.44 + 5.14
50.99 + 1.47
75.45 + 1.88
71.53 0.70
ChebNet
34,40 1 1.18
31.75 1: 3,42
34,30 4.333
91.60 1 0.44
81.58 1 5.09
65.18
8.37
GCN
30.21 1: 0.86
35.57 1.86
4..36
72.32
87,50 1.68
75.77 0.96
SGC
29.26 141
2.12
41.40
0.91
88.05 - 2.08
75.80 1.75
GAT
2.99
3.074
2..0
87.59
87.11 1.48
76.43 1.31
GraphSAGE
34.95 + 1.06
2.68
4
90.54 0.66
87.90 1.73
76.43 - 1.19
GIN
28.29 + 1.45
39.51
40.77
75.89
85.65 +: 2.26
72.55 1.78
APPNP
33.68 1 1.26
2.335
4.333
67.36
87.59 1.68
75.90 0.91
GCNII
34.78 1.50
35.93
2.75
0.85
87.20
73.84 0091
GATv2
28.87
+
6.60
88.85
87.66
76.59
MixIlop
35,40 1.34
30.43
37.93
89.68
84.53 1.53
76.11 0.83
TAGCN
34.92 1.19
2.33
41.01 3,77
0.55
88.38 1.95
76.49 1.41
DAGNN
33.15 1.14
2.55
38.94
67.87
88.27 1.53
75.81 = 0.90
JKNet
28.63 0.94
40.81 1 2.60
40.39 4.85
0.92
86.24 - 0.85
73.11 = 1.82
Virtual Node
30.71
3.200
41.45
72.36
87.24
69.80
H2GCN
34.20 1.47
3.15
3.13
87.08 0.82
76.89 + 2.25
75.87 1.02
PSGNN
35.60 1 1.34
37.28
3.62
1.000:0.00
87.81 + 1.96
76.77 = 1.13
ACM-GNN
34.04 1.25
2.
4.,78
88.58 = 1.90
76.47 0.99
FAGCN
36 1.52
36.52 1.72
39.83
84.69
88.66
76.82 1488
OrderedGNN
35.64 0.98
32.70
38.38 3.65
91.01
84.81 + 1.67
74.10 1.62
GloGNN
19.80
2..72+22.3
40.17
53.42
73.02
72.46
GGCN
32.76 1.39
35.06 5.66
34.08 3.44
84.76 1.874
86.39 :: 1.93
75.36
1.99
GPRGNN
3
34.97
2.83
40,50
83.94
88,86 + 1.42
76.49
1.00
ALT
33.10 1.38
37.28 21 1.49
39.61 3.36
0.66
88.82 + 2.02
76.88
1.20
NodeFormer
29.26 2.31
24.29 2.60
4.008
77.71
87.44 = 1.37
75.20 = 1.27
SGFormer
25.89 + 0.80
34.54 2.96
42.79 4.06
$2.06 0.50 0.50
86.24 + 1.58
70.74 1.25
DIFFormer
33.17 2.84
39.16 4.10
69.25 0.93
76.65 1.52
GRAPHITE (Ours)
37.69 1.57
43.06 2.89
45.08 4.04
94.78 0.41
88.23 1.65
76.41 1.57
4.1
EXPERIMENTAL SETTINGS
Datasets. We evaluate GRAPHITE and various baseline methods across six real-world datasets. The dataset statistics summarized in Table The reported homophily is the adjusted homophily introduced in Platonov et al. (2024). which exhibits more desirable properties compared to tradi- tional edge/node homophily. We leverage adjusted homophily categorize the datasets into two groups: heterophilic and homophilic. Please see Appendix A.1 for dataset descriptions.
Training and evaluation. To benchmark GRAPHITE and compare it with the baseline methods, we use node classification tasks with performance measured by classification accuracy on ACTOR, CHAMELEON-F, SQUIRREL-F. CORA, and CITESEER and by ROC-AUC on MINESWEEPER fol- lowing Platonov et al. 2023). For all baseline methods, we use the hyperparameters provided by the ACTOR, CHAMELEON-F, and SQUIRREL-F, we generate 10 random
authors. For the evaluation splits with a ratio of 48%/30 33% 200 as the training/validationnnent set, respectively, following Gu directly utilize the 10 random splits pro- vided by the original paper (Platonov et al. 2023). For the evaluation on CORA and CITESEER, we follow Luan et al. (202 and al. 20200) randomly generate 10 random splits with a ratio set, respectively. For each experiment, we report the mean and the standard deviation of the performance metric across the corresponding 10 random splits. Please see Appendix A for additional experimental settings.
let al. (2024). For the evaluation MINESWEEPER,
of 60%/20% 220% 20% as the mmuning/validationn/ee
4.2 MAIN RESULTS
To answer RQT we compare the proposed method GRAPHITE with 27 state-of-the-un methods on six heterophilic and homophilic graphs. The results shown in Table 2

---

Published as a conference paper at ICLR 2026
As shown in Table 2 our GRAPHITE achieves significant performance gains (p-value=(0.) over prior state-of-the-an art GNN methods on heterophilic graphs while maintaining competitive accu- racy on homophilic graphs. Specifically, GRAPHITE outperforms the best baseline methods by 4.17%, 5.23%. 5.35% and 3.47% on ACTOR, SQUIRREL-FF, CHEMELEON-F and MINESWEEPERS respectively. While some existing models perform well individual datasets, they often strug- gle on others, highlighting their insufficient consistency. In contrast, GRAPHITE demonstrates the best results across all four heterophilic benchmarks. Another interesting observation is that while GRAPHITE is built upon FAGCN (Bo et al.) (02T). it significantly surpasses FAGCN, demonstrat- ing the beneficial effect of our graph transformation and feature edges.
Discussion. It is worth noting that most of the baseline methods cannot achieve better results com- pared to MLP on ACTOR, which can be explained by the fact that these methods typically treat node features and graph structure as joint input without explicitly decoupling them. The weak struc- tural homophily exhibited by ACTOR makes typical GNNs fail to capture important feature signals, reinforeing the importance of our graph transformation strategy that boosts feature homophily sig- nificantly. For SQUIRREL-F, reveals that structure information is very important within SQUIRREL- since JKNet aggregates feature knowledge from multi-hop neighbors learn structure-aware representation. This finding also explains the success of GRAPHITE since the useful multi-hop information in SQUIRREL- can be propagated even more efficiently through the constructed feature edges.
find that JKNet is the best among baselines. This observation
As another example, SGFormer performs the best on CHAMELEON-F among baseline methods. We argue that CHAMELEON-F needs a considerable amount of global messages and graph trans- formers are experts at capturing this type of information. Compared with NodeFormer and DIF- Former, SGFormer is the most advanced graph transformer utilizing simplified graph attention that strikes good balance between global structural information and feature signal, preventing the over- globalizing issue (Xing et al. 2024). Similarly, GRAPHITE transforms the original graph into a form that facilitates global message exchange by the introduction of feature edges. As final re- mark, although GRAPHITE is designed specifically to deal with heterophilic datasets, GRAPHITE still maintains competitive accuracy homophilic datasets (CORA and CITESHER), achieving sults that are on par with the best existing methods.
4.3 HOMOPHILY ANALYSIS
Table 3: Relative improvement ratio of feature ho- mophily and adjusted homophily across datasets.
To answer RQ2, we conduct homophily anal-
ysis across heterophilic datasets under two ho- mophily metrics: feature homophily featuue (g)
GRAPHITE significantly boosts both homophily metrics. See Figure 2 for visualization.
and adjusted homophily Hadjussed (g) (see Ap- pendix B for their formal definitions). Table 3 and Figure 2 show the relative improvements between the homophily metrics before and af- ter applying GRAPHITE. We can observe a sig- nificant boost in both homophilily metries af- ter applying GRAPHITE across the four het- erophilic datasets.
Dataset
Hisature (g)
Haljussod (g)
ACTOR SQUIRREL-F
+179%
+2767%
+961% %
+215%
CHAMELHON-F
+1739%
+402%
MINESWEEPER
+41%
+1023%
Discussion. Overall, GRAPHITE effectively boosts both homophily metrics across all heterophilic datasets. Specifically, Squirrel-F and Chameleon-F demonstrate significant boosts in terms of fea- ture homophily. This is mainly because their discrete features directly correspond to specific top- ics and each feature edge will contribute much higher feature similarity than usual edges. On the other hand, Actor and Minesweeper showcase much higher adjusted homophily after apply- ing GRAPHITE. For Actor, this favorable behavior can be attributed to the high correlation between page co-occurrences and node labels; while for Minesweeper, the sum of label-specific node degrees (defined in Equation (14h) increases much due to the transformation performed by GRAPHITE.
Baseline methods. In experiments, consider wide range of GNN baselines, including MLP aggnosstee-. homophilic GNNs, heterophilic GNNs, and Graph Transformers. The full list is shown in Appendix A.2
8

---

Published as a conference paper at ICLR 2026
Table Effectiveness of the proposed graph transformation. GRAPHITE transformed graphs alone can already enhance the performance of homophilic GNNs.
Dataset
ACTOR
MINESWEEPER
+GRAPHITE?
GCN
30.21 0.86
34.83 1.28
72.32
0.93
75.38 + 1.56
GAT
28.86 0.99
32.09 1.35
87.59 1.35
88.66
- 0.88
GraphSAGE
34.95 -: 1.06
35.09 1.06
90.54 0.66
90.85 0.67
JKt
28.63 0.94
35.96+1..0
81.00 + 0.92
85.56 2.59
GIN
28.29 + 1.45
33.75 1.83
75.89 + 2.09
87.07 1.71
Table Comparison aggregators for the features of feature nodes. All aggregators fact perform similarly, so we choose averaging due to its simplicity and efficiency.
Dataset
Averaging (Ours)
Learned Embeddings
Learned Attention
Majority Voting
ACTOR
37.69
37.46
37.13
37.59
SQUIRREL-F
43.06
43.53
43.46
43.28
CHAMELEON-F
45.08
44.80
44.36
45.64
MINESWFEPER
94.78
94.47
94.56
94.75
4.4 ABLATION STUDIES
Graph transformation. To further demonstrate the effectiveness of our proposed graph transforma- tion GRAPHITE and answer RQ3 we compare the performance of homophilic GNNs on the orig- inal graph and that on the transformed graph. In this experiment, we use two larger-scale datasets, ACTOR and MINESWEEPER, and five representative homophilic GNNs, GCN, GAT, GraphSAGE, JKNet, and GIN. The results are presented in Table 4
see that proposed GRAPHITE consistently improves the performance of the five representative homophilic GNNs on both datasets, even though these GNNs are not specially designed for modeling feature nodes. For example. the accuracy of GAT ACTOR is enhanced from 30.21% to 34.83%, which is relative improvement of 15.29%. The results demonstrate that proposed graph transformation GRAPHITE significantly enhance the performance of homophilic GNNs on originally heterophilic graphs, echoing the fact that our proposed graph trans- formation can significantly increase the graph homophily.
From Table 4
Features of feature nodes. GRAPHITE uses averaging aggregation to define the features of fea- ture nodes. To elaborate on the rationale this simple aggregator and answer RQ4 compare with other aggregators on heterophilic datasets. The results are shown in Table 5 In fact, all ag- gregators have similar accuracies while averaging is more efficient than learned embeddings and attention-weigee weighted aggregation and is simpler than majority voting. Therefore, we use averaging in our method due to its simplicity and efficiency.
Computational efficiency. To evaluate the efficiency of our method and answer RC5) we provide a comparison of running times with and without our transformation, respectively. The results are shown in Table 6 (sorted by graph sizes). We can see that our graph transformation has only small impact on running time while significantly improving accuracy. Notably, the computational overhead gets smaller larger graphs (e.g., Minesweeper), justifying the scalability of our method. This is because the number of added nodes (i.c., the number of features) is typically negligibly small compared with the number of nodes large graphs, and the number of feature edges is proportional to the space complexity of the node feature matrix X.
Hyperparameters. Since method has few hyperparameters including and wxx to answer RQ6) we provide a sensitivity analysis of them on MINESWEEPER. The results are shown in Table From the table, can see that method is sensitive these hyperparameters, and our method consistently outperform the best baseline under various hyperparameter values.
5 RELATED WORK
A substantial body of research has explored the challenges heterophily in graph neural networks (GNNs). Many early approaches sought improve information aggregation, such MixHop (Abu-)

---

Published as a conference paper at ICLR 2026
Table 6: Running time comparison with and without GRAPHITE. Graphs are sorted in decreasing order by graph sizes. Our graph transformation has only small impact on running time while significantly improves accuracy.
Method
MINESWEEPER
ACTOR
SQUIRREL-F
CHAMELEON-F
No transformation
1.9 min
1.5 min
min
0.5 min
With transformation
2.3 min
2i0
1.1 min
0.7 min
Table 7: Sensitivity analysis of hyperparameters т and wx on MINESWEEPER, Our method consis- tently outperform the best baseline under various hyperparameters.
Best
GRAPHITE (Ours)
Baseline
= 0.1
T 0.5
7
1.0
7
1.5
T
2.0
91.60
93.48
94,29
94.78
94.66
94.18
Best
ARAPHITE (Ours)
Baseline
= 0.1
W0 = 0.25
0.5
wx
0.75
1.0
91.60
94.78
94.16
93.95
93.53
93.15
El-Haija et al. 2019), which mixes different-hop neighborhood features, and GPRGNN (Chien et al.] 2020), which employs generalized PageRank propagation for adaptive message passing. Other methods focus explicit heterophilic adaptations, such H2GCN (Zhu et al. 2020), which sep- arates ego- and wrighbor-embeddingss and FAGCN (Bo et al. (021). which learns optimal repre- sentations via frequency-adaptiv filtering. Additional works, including OrderedGNN (Song al. 2023), GloGNN (Li et al. 2022). and GGCN (Yan et al. 2022), leverage structural ordering, global context, and edge corrections, respectively, to enhance performance on heterophilic graphs. Recent advances explore alternative formulations. such as component-wise signal decomposition (e.g. ALT, Xu et al., 2023) and adaptive residual mechanisms (Xu et al. [024a] Yan et al., 2024) for greater flexibility. Beyond architectural innovations, rigorous benchmarking efforts (Lim et al.) 2021 Zhu ctt al. 2024 Platonov et al. 2023) have been introduced to standardize evaluations and assess gener- alization across diverse graph properties. A broader synthesis of heterophilic GNN techniques can be found in recent surveys (Zheng et al. 2022 Zhu et al. 2023 Luan et al. 2024
Gong et al. 2024).
Please refer Appendix C for additional related work.
CONCLUSION & FUTURE WORK
In this paper, propose GRAPHITE, simple yet efficient framework to address the heterophily issue in node classification. By introducing feature nodes that connect to graph nodes with corre- sponding discrete features, ratio. Through theoretical analysis and empirical study, we validate that GRAPHITE can indeed effectively increase the graph homophily. Our extensive experiments demonstrate that GRAPHITE consistently outperforms state-of-the-art methods on heterophilic graph datasets and achieves com- parable performance on homophilic graph datasets. An interesting future direction would be extend- ing the proposed graph transformation to general graphs with continuous node features; possible approaches include clustering the continuous features into a few clusters and binning the continuous features into discrete buckets. Other future directions include (i) studying how our graph transfor- mation affects graph properties, (ii) connecting to the node distinguishability principle (Luan al.) 2023). and (iii) identifying optimal subset of features (Zheng et al. 2025).
solve the heterophily issue by increasing the graph homophily
ACKNOWLEDGEMENTS
This work is supported by NSF (2416070). The content of the information in this document does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.
10

---

Published as a conference paper at ICLR 2026
ETHICS STATEMENT
Our study is based entirely on publicly available graph datasets commonly used in the GNN literature and does not involve private or sensitive information. We develop graph transformation framework that explicitly increases graph homophily to enable more effective message passing. To ensure methodological soundness and reproducibility, we provide both theoretical analyses and extensive empirical evaluations across heterophilic datasets. The release of code and data splits is intended solely for academic research to advance the understanding of graph machine learning and to support future work on graph neural networks, and are not designed for sensitive or high-stakes applications.
REPRODUCIBILITY STATEMENT
We include the conceptual framework, transformation steps, method details and evaluation setup in the paper and appendix. To facilitate reproducibility, we also publicly release our code at https: 7/github. com/co////////oo q rz/ r ICLR26 GRAPHITE
REFERENCES
Sami Abu-El-Hae El-Haaaa, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In Proceedings of the 36th International Con- ference Machine Learning, pp. 21-29. PMLR, 2019.
Uri Alon and Eran Yahav. On the bottleneck of graph neural networks and its practical implications. arXiv preprint, 2020.
Adrián AmaizzRodrríuuz, Ahmed Begga, Francisco Escolano, and Nuria Oliver. DiffWire: Induc- tive graph rewiring via the lovász bound. arXiv preprint, 2022.
Wenxuan Bao, Ruxi Deng, Ruizhong Qiu, Tianxin Wei, Hanghang Tong, and Jingrui He. Latte: Col- laborative test-time adaptation of vision-language models federated learning. In Proceedings of the IEEE/CVF International Conference Computer Vision, 2025.
Federico Barbero, Ameya Velingker, Amin Saberi, Michael Bronstein, and Francesco Di Giovanni. Locality-aware graph-rewiring in gnns. arXiv preprint, 2023.
Burak Bartan, Ruizhong Qiu, Rafael Esteves, Yuwei Ren, Weiliang Will Zeng, and An Chen. FineAMP: optimization-based automatic mixed precision quantization for efficient diffusion model inference. The 17th International OPT Workshop Optimization for Machine Learn- ing. 2025.
Ali Behrouz and Farnoosh Hashemi. Graph Mamba: Towards learning on graphs with state space models. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 119-130, 2024.
Mitchell Black, Zhengchao Wan, Amir Nayyeri, and Yusu Wang. Understanding oversquashing in GNNs through the lens of effective resistance. In Proceedings of the 40th International Confer- on Machine Learning, pp. 2528 2547. PMLR, 2023.
Deyu Bo, Xiao Wang. Chuan Shi, and Huawei Shen. Beyond oww-ureuyncy information in graph convolutional networks. In Proceedings of the AAAI Conference on Artificial Intelligence, vol- ume 35, pp. 3950-3957, 2021.
Shaked Brody, Uri Alon, and Eran Yahav. How attentive are graph attention networks? preprint, 2021.
arXiv
Eunice Chan, Zhining Liu, Ruizhong Qiu, Yuheng Zhang, Ross Maciejewski, and Hanghang Tong. Group faimeess via group consensus. In The 2024 ACM Conference on Fairness, Accountability and Transparency, pp. 1788-1808, 2024.
Lingjie Chen, Ruizhong Qiu, Siyu Yuan, Zhining Liu, Tianxin Wei, Hyunsik Yoo, Zhichen Zeng, and Deqing Yang. WAPITI: watermark for finetuned open-source LLMSs. arXiv preprint, 2024.
11

---

Published as a conference paper at ICLR 2026
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. Simple and deep graph con- volutional networks. In Proceedings of the 37th International Conference on Machine Learning, pp. 1725-1735. PMLR, 2020.
Sirui Chen, Yunzhe Qi, Mengting Ai, Yifan Sun, Ruizhong Qiu, Jiaru Zou, and Jingrui He. muftuence-preservinn proxies for gradient-based data selection in LLM finetuning. In The Four- teenth International Conference on Learning Representations, 2026.
Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. Adaptive universal generalized PageRank graph neural network. arXiv preprint, 2020.
Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng, Zhining Liu, Xuying Ning, Duo Zhou, and Jingrui He. AdaFuse: adaptive ensemble decoding with test-time scaling for LLMs. arXiv preprint, 2026.
Andreea Deac, Mare Lackenby, and Petar Veličković. Expander graph propagation. In Learning on Graphs Conference, pp. 38-1. PMLR, 2022.
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances Neural Information Processing Systems, volume 29, 2016.
Francesco Di Giovanni. Lorenzo Giusti, Federico Barbero, Giulia Luise, Pictro Lio, and Michael M. Bronstein. On over-squashing in message passing neural networks: The impact of width, depth, and topology. In Proceedings of the 40th International Conference on Machine Learning, pp. PMLR, 2023.
Yanfei Dong, Mohammed Haroon Dupty, Lambert Deng, Zhuanghua Liu, Yong Liang Goh, and Wee Sun Lee. Differentiable cluster graph neural network. arXiv preprint, 2024.
Jian Du, Shanghang Zhang, Guanhang Wu, José MF Moura, and Soummya Kar. Topology adaptive graph convolutional networks. arXiv preprint, 2017.
Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Günnemann. Predict then propagate: Graph neural networks meet personalized pagerank. In The Seventh International Conference on Learning Representations, 2019.
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. Neural message passing for quantum chemistry. In Proceedings of the 34th International Conference on Machine Learning, pp. 1263-1272. PMLR, 2017.
Chenghua Gong, Yao Cheng, Jianxiang Yu, Can Xu, Caihua Shan, Siqiang Luo, and Xiang Li. A survey on learning from graphs with heterophily: Recent advances and future directions. arXiv preprint, 2024.
Ming Gu, Zhuonan Zheng, Sheng Zhou, Meihan Liu, Jiawei Chen, Tanyu Qiao, Liangcheng Li, and Jiajun Bu. Universal inceptive GNNs by eliminating the smoothness-eeneralizattoo dilemma. arXiv preprint, 2024.
Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems, volume 30, 2017.
Xinyu He, Jian Kang, Ruizhong Qiu, Fei Wang, Jose Sepulveda, and Hanghang Tong. On the sensitivity of individual faimess: Measures and robust algorithms. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pp. 329-838, 2024.
Xinyu He, Chenhan Xiao, Haoran Li, Ruizhong Qiu, Zhe Xu, Yang Weng, Jingrui He, and Hanghang Tong. PowerGrow: feasible co-growth of structures and dynamics for power grid synthesis. In Proceedings of the 32nd ACM SIGKDD Conference Knowledge Discovery and Data Mining, 2026.
Dan Hendrycks and Kevin Gimpel. Gaussian error linear units. arXiv preprint, 2016.
12

---

Published as a conference paper at ICLR 2026
Di Jin, Rui Wang, Meng Ge, Dongxiao He, Xiang Li, Wei Lin, and Weixiong Zhang. RAW-GNN: Random walk aggregation based graph neural network. In Proceedings of the 31st International Joint Conference on Artificial Intelligence, pp. 2108-2114 2022.
Baoyu Jing, Sanhorn Chen, Lecheng Zheng, Boyu Liu, Zihao Li, Jiaru Zou, Tianxin Wei, Zhining Liu, Zhichen Zeng, Ruizhong Qiu, Xiao Lin, Yuchen Yan, Qi Yu, Dongqi Fu, Jingrui He, and Hanghang Tong. TRQA: time series reasoning question and answering benchmark. OpenReview, 2025.
Kedar Karhadkar, Pradeep Kr Banerjee, and Guido Montúfar. FoSR: First-order spectral rewiring for addressing oversquashing in gnns. arXiv preprint, 2022.
Diederik P. Kingma and Jimmy Ba. Adam: method for stochastic optimization. arXiv preprint, 2014.
Thomas N Kipf and Max Welling, Semi-supervised classification with graph convolutional net-
works. arXiv preprint arXiv: 1609.02900 2016.
Gaotang Li, Marlena Duda, Xiang Zhang. Danai Koutra, and Yujun Yan. Interpretable sparsification of brain graphs: Better practices and effective designs for graph neural networks. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1223- 1234, 2023a.
Gaotang Li, Ruizhong Qiu, Xiusi Chen, Heng Ji, and Hanghang Tong. Beyond log likelihood: Probability- based objectives for supervised fine-tuning across the model capability continuum. arXiv preprint, 2025a.
Mufei Li. Dongqi Fu, Limei Wang, Si Zhang, Hanqing Zeng. Kaan Sancak, Ruizhong Qiu, Haoyu Peter Wang. Xiaoxin He, Xavier Bresson, Yinglong Xia, Chonglin Sun, and Pan Li. Haystack engineering: Context engineering meets the long-context challenge in large language models. NeurlPS 2025 Workshop on Evaluating the Evolving LLM Lifecycle: Benchmarks, Emer- gent Abilities, and Scaling, 2025b.
Ting Wei Li, Qiaozhu Mei, and Jiaqi Ma. A metadata-driven approach to understand graph neural networks. Advances in Neural Information Processing Systems, 11555-153340000 2023b.
Ting- Wei Li, Ruizhong Qiu, and Hanghang Tong. Graph data selection for domain adaptation: A
Wing-Wei le model-free approach. In Advances in Neural Information Processing Systems 38, 2025c.
Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, and Weining Qian. Finding global homophily in graph neural networks when meeting heterophily. In Proceedings of the 39th International Conference on Machine Learning, pp. 12242-13252 PMLR, 2022.
Xiao Li, Li Sun, Mengjie Ling, and Yan Peng. A survey of graph neural network based recommen- dation in social networks. Neurocomputing, 549:126441, 2023c.
Zihao Li, Zhichen Zeng, Xiao Lin, Feihao Fang. Yanru Qu, Zhe Xu, Zhining Liu, Xuying Ning, Tianxin Wei, Ge Liu, Hanghang Tong, and Jingrui He. Flow matching meets biology and life science: survey. arXiv preprint, 2025d.
Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, and Ser Nam Lim. Large scale learning non-homophilous graphs: New benchmarks and strong simple methods. In Advances in Neural Information Processing Systems, volume 34, pp. 20887- 20902, 2021.
Xiao Lin, Zhining Liu, Dongqi Fu, Ruizhong Qiu, and Hanghang Tong. BackTime: Backdoor attacks multivariate time series forecasting. In Advances in Neural Information Processing Systems, volume 37, 2024.
Xiao Lin, Zhining Liu, Ze Yang, Gaotang Li, Ruizhong Qiu, Shuke Wang, Hui Liu, Haotian Li, Sumit Keswani, Vishwa Pardeshi, Huijun Zhao, Wei Fan, and Hanghang Tong. MORALISE: a structured benchmark for moral alignment in visual language models. arXiv preprint, 2025.
13

---

Published as a conference paper at ICLR 2026
Xiao Lin, Zhicheng Tang. Weilin Cong, Mengyue Hang, Kai Wang. Yajuan Wang. Zhichen Zeng, Ting- Wei Li, Hyunsik Yoo, Zhining Liu, Xuying Ning, Ruizhong Qiu, Wen- Yen Chen, Shuo Chang, Rong Jin, Huayu Li, and Hanghang Tong. Mixture of sequence: Theme-awan mixture- of-experts for long-sequence recommendation. In Proceedings of the ACM Web Conference 2026, 2026.
Lihui Liu, Zihao Wang, Ruizhong Qiu, Yikun Ban, Eunice Chan, Yangqiu Song, Jingrui He, and Hanghang Tong. Logic query of thoughts: Guiding large language models to answer complex logic queries with knowledge graphs. arXiv preprini, 2024a.
Meng Liu, Hongyang Gao, and Shuiwang Ji. Towards deeper graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp- 338 348, 2020.
Zhining Liu, Zhichen Zeng, Ruizhong Qiu, Hyunsik Yoo, David Zhou, Zhe Xu, Yada Zhu, Kommy Weldemariam, Jingrui He, and Hanghang Tong. Topological augmentation for class-iballanced node classification, 2023.
Zhining Liu, Ruizhong Qiu, Zhichen Zeng, Hyunsik Yoo, David Zhou, Zhe Xu, Yada Zhu, Kommy Weldemariam, Jingrui He, and Hanghang Tong. Class-imbalanced graph learning without class rebalancing. In Proceedings of the 41st International Conference on Machine Learning, 2024b.
Zhining Liu, Ruizhong Qiu, Zhichen Zeng, Yada Zhu, Hendrik Hamann, and Hanghang Tong. AIM: Attributing, interpreting, mitigating data unfairness. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2014-2025, 2024c.
Zhining Liu, Ziyi Chen, Hui Liu, Chen Luo, Xianfeng Tang, Suhang Wang, Joy Zeng, Zhenwei Dai, Zhan Shi, Tianxin Wei, Benoit Dumoulin, and Hanghang Tong. Seeing but not believing: Probing the disconnect between visual attention and answer correctness in vims. arXiv preprint, 2025a.
Zhining Liu, Ze Yang, Xiao Lin, Ruizhong Qiu, Tianxin Wei, Yada Zhu, Hendrik Hamann, Jin- grui He, and Hanghang Tong. Breaking silos: Adaptive model fusion unlocks better time series forecasting. In Proceedings of the 42nd International Conference on Machine Learning, 2025b.
Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Kiao-Wen Chang, and Doina Precup. heterophily a real nightmare for graph neural networks to do node classification? arXiv preprint, 2021.
Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, and Doina Precup. Revisiting heterophily for graph neural networks. In Advances in Neural Information Processing Systems, volume 35, pp. 1362-1375, 2022.
Sitao Luan, Chenqing Hua, Minkai Xu, Qincheng Lu, Jiaqi Zhu, Xiao Wen Chang, Jie Fu, Jure Leskovec, and Doina Precup. When do graph neural networks help with node classification? in- vestigating the homophily principle on node distinguishabilityy In Advances in Neural Information Processing Systems, volume 36, pp. 28748-28760, 2023.
Sitao Luan, Chenqing Hua, Qincheng Lu, Liheng Ma, Lirong Wu, Xinyu Wang, Minkai Xu, Xiao- Wen Chang, Doina Precup, Rex Ying, Stan Z. Li, Jian Tang, Guy Wolf, and Stefanie Jegelka. The heterophilic graph learning handbook: Benchmarks, models, theoretical analysis, applications and challenges. arXiv preprint, 2024.
Jiaqi Ma, Xingjian Zhang, Hezheng Fan, Jin Huang, Tianyue Li, Ting- Wei Li, Yiwen Tu, Chen- shu Zhu, and Qiaozhu Mei. Graph learning indexer: A contributor-friendly and metadata-rich platform for graph learning benchmarks. In Learning on Graphs Conference, pp. 7-1. PMLR, 2022.
Sunil Kumar Maurya, Xin Liu, and Tsuyoshi Murata. Improving graph neural networks with simple architecture design. arXiv preprint, 2021.
Khang Nguyen, Nong Minh Hieu, Vinh Due Nguyen, Nhat Ho, Stanley Osher, and Tan Minh Nguyen. Revisiting smor-smo and over-squashing using ollivier-rieci curvature. In Pro- ceedings of the 40th International Conference on Machine Learning, pp. 15956-259799 PMLR, 2023.
14

---

Published as a conference paper at ICLR 2026
Shashank Pandit, Duen Horng Chau, Samuel Wang, and Christos Faloutsos. Netprobe: fast and scalable system for fraud detection in online auction networks. In Proceedings of the 16th Inter- national Conference World Wide Web, pp. 201-210, 2007.
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. Geom-GCN: Geo- metric graph convolutional networks. In The Eighth International Conference on Learning Rep- resentations, 2020.
Oleg Platonov, Denis Kuznedelev, Michael Diskin, Artem Babenko, and Liudmila Prokhorenkova. A critical look at the evaluation of GNNs under heterophily: Are we really making progress? In The Eleventh International Conference on Learning Representations, 2023.
Oleg Platonov, Denis Kuznedelev, Artem Babenko, and Liudmila Prokhorenkova. Characterizing graph datasets for node classification: Homophily-heterophiiy dichotomy and beyond. In Ad- vances Neural Information Processing Systems, volume 36, 2024.
Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van den Broeck, Mathias Niepert, and Christopher Morris. Probabilistically rewired message-passing neural networks. arXiv preprint, 2023.
Ruizhong Qiu, Zhiqing Sun, and Yiming Yang. DIMES: A differentiable meta solver for combina- torial optimization problems. In Advances in Neural Information P Processing Systems, volume 35, pp. 25531-25546, 2022.
Ruizhong Qiu, Dingsu Wang, Lei Ying, H Vincent Poor, Yifang Zhang, and Hanghang Tong. Re- constructing graph diffusion history from single snapshot. In Proceedings the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1978-1988, 2023.
Ruizhong Qiu, Jun-Gi Jang, Xiao Lin, Lihui Liu, and Hanghang Tong. TUCKET: A tensor time series data structure for efficient and accurate factor analysis over time ranges. Proceedings of the VLDB Endowment, 17(13), 2024.
Ruizhong Qiu, Gaotang Li, Ting- Ting-Wei Li, Tianxin Wei, Jingrui He, and Hanghang Tong. Efficient inference scaling for safety assurance. NeurlPS 2025 Workshop on Vision Language Model Real- World Deployment, 2025a.
Ruizhong Qiu, Zhe Xu, Wenxuan Bao, and Hanghang Tong. Ask, and it shall be given: On the Tur- ing completeness of prompting. 13th International Conference on Learning Representations, 2025b.
Ruizhong Qiu, Weiliang Will Zeng, Hanghang Tong, James Ezick, and Christopher Lott. How efficient is LLM--generatd code? A rigorous & high-standard benchmark. In 13th International Conference Learning Representations, 2025c.
Ruizhong Qiu, Hanqing Zeng, Yinglong Xia, Yiwen Meng, Ren Chen, Jiarui Feng, Dongqi Fu, Qi- fan Wang, Jiayi Liu, Jun Xiao, Xiangjun Fan, Benyu Zhang, Hong Li, Zhining Liu, Hyunsik Yoo, Zhichen Zeng, Tianxin Wei, and Hanghang Tong. ReMix: reinforcement routing for mixtures of LoRAs in LLM finetuning, arXiv preprint, 2026.
Manon Réau, Nicolas Renaud, Li C. Xue, and Alexandre M.J.J, Bonvin. DeepRank-GNNy A graph neural network framework to learn patterns in protein-protein interfaces. Bioinformatics, 39(1), 2023.
Benedek Rozemberczki, Carl Allen, and Rik Sarkar. Multi-scale attributed node embedding. Journal of Complex Networks, 9(2), 2021.
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. Collective classification in network data. Al Magazine, 29(3):33-9,, 2008.
Dai Shi, Yi Guo, Zhiqi Shao, and Junbin Gao. How curvature enhance the adaptation power of framelet GCNs. arXiv preprint, 2023a.
Dai Shi, Andi Han, Lequan Lin, Yi Guo, and Junbin Gao. Exposition on over over- weurssiushin problem GNNs: Current methods, benchmarks and challenges. arXiv preprint, 2023b.
15

---

Published as a conference paper at ICLR 2026
Yunchong Song, Chenghu Zhou, Xinbing Wang, and Zhouhan Lin. Ordered GNN: Ordering mes- sage passing to deal with heterophily and svor-smoot In The Eleventh International Confer- ence on Learning Representations, 2023.
Jie Tang, Jimeng Sun, Chi Wang, and Zi Yang. Social influence analysis in large-scale networks. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 807-816, 2009.
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M. Bronstein. Understanding over-squashing and bottlenecks on graphs via curvature. arXiv preprint,
2021.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks, arXiv preprint arXiv:1710.10000 2017.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pictro Liò, and Yoshua Bengio. Graph attention networks. In The Sixth International Conference Learning Represen- tations, 2018
Dingsu Wang, Yuchen Yan, Ruizhong Qiu, Yada Zhu, Kaiyu Guan, Andrew Margenot, and Hang- hang Tong. Networked time series imputation via position-aware graph enhanced variational autoencoders. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2256-2268, 2268, 2023.
Tianxin Wei, Yuning You, Tianlong Chen, Yang Shen, Jingrui He, and Zhangyang Wang. Aug- mentations in hypergraph contrastive learning: Fabricated and generative. Advances in neural 1922, 2022.
information processing systems,
Tianxin Wei, Ruizhong Qiu, Yifan Chen, Yunzhe Qi, Jiacheng Lin, Wenju Xu, Sreyashi Nag, Ruirui Li, Hanqing Lu, Zhengyang Wang, Chen Luo. Hui Liu, Suhang Wang, Jingrui He, Qi He, and Xianfeng Tang. Robust watermarking for diffusion models: unified multi-dimensional recipe, 2024.
Tianxin Wei, Xuying Ning, Xuxing Chen, Ruizhong Qiu, Yupeng Hou, Yan Xie, Shuang Yang. Zhi- gang Hua, and Jingrui He. CoFiRec: coarse-to-fine tokenization for generative recommendation. arXiv preprint, 2025a.
Tianxin Wei, Noveen Sachdeva, Benjamin Coleman, Zhankui He, Yuanchen Bei, Xuying Ning, Mengting Ai, Yunzhe Jingrui He, Ed H. Chi, Chi Wang, Shuo Chen, Fernando Pereira, Wang- Cheng Kang. and Derek Zhiyuan Cheng. Evo-Memory: Benchmarking LLM agent test-time learning with self-evolving memory. arXiv preprint, 2025b.
Tianxin Wei, Ting-Wei Wei Li, Zhining Liu, Xuying Ning, 7e Yang, Jiaru Zou, Zhichen Zeng. Ruizhong Qiu, Xiao Lin, Dongqi Fu, Zihao Li, Mengting Ai, Duo Zhou, Wenxuan Bao, Yunzhe Li, Gaotang Li. Cheng Qian, Yu Wang. Xiangru Tang, Yin Xiao, Liri Fang. Hui Liu, Xianfeng Tang. Yuji Zhang, Chi Wang, Jiaxuan You, Heng Ji, Hanghang Tong, and Jingrui He. Agentic reasoning for large language models: survey. arXiv preprint, 2026a.
Tianxin Wei, Ruizhong Qiu, Yifan Chen, Yunzhe Qi, Jiacheng Lin, Wenxuan Bao, Wenju Xu, Sreyashi Nag, Ruirui Li. Hanqing Lu, Zhengyang Wang, Chen Luo. Hui Liu, Suhang Wang, Jingrui He, Qi He, and Xianfeng Tang. DifIKGW: stealthy and robust diffusion model water- Machine Learning Research, 2026b.
marking. Transactions
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinherger. Sim- plifying graph convolutional networks. In International conference on machine learning, pp. 6861-6871, PMLR, 2019.
Qitian Wu, Wentao Zhao, Zenan Li, David Wipf, and Junchi Yan. NodeFormer: scalable graph structure learning transformer for node classification. In Advances in Neural Information Processing Systems, volume 35, pp. 27387-27401, 2022.
Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, and Junchi Yan. Difformer: Scalable (graph) transformers induced by energy constrained diffusion. arXiv preprint, 2023.
16

---

Published as a conference paper at ICLR 2026
Qitian Wu, Wentao Zhao, Chenxiao Yang, Hengrui Zhang, Fan Nie, Haitian Jiang, Yatao Bian, and Junchi Yan. Simplifying and empowering transformers for sare-graphy representations. In Advances in Neural Information Processing Systems, volume 36, 2024a.
Ziwei Wu, Lecheng Zheng, Yuancheng Yu, Ruizhong Qiu, John Birge, and Jingrui He. Fair anomaly detection for imbalanced groups, arXiv preprint, 2024b.
Yujie Xing, Xiao Wang, Yibo Li, Hai Huang, and Chuan Shi. Less is more: On the over-globalizing problem in graph transformers. arXiv preprint, 2024.
Haobo Xu, Yuchen Yan, Dingsu Wang, Zhe Xu, Zhichen Zeng, Tarek F. Abdelzaher, Jiawei Han, and Hanghang Tong. SLOG: An inductive spectral graph neural network beyond polynomial filter. In Proceedings of the 41st International Conference on Machine Learning, 2024a.
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? The Sixth International Conference on Learning Representations, 2018a.
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. Representation learning on graphs with jumping knowledge networks. In Proceedings of the 35th International Conference on Machine Learning, pp. 5453-5462. PMLR, 2018b.
Zhe Xu, Yuzhong Chen, Qinghai Zhou, Yuhang Wu, Menghai Pan, Hao Yang, and Hanghang Tong. Node classification beyond homophily: Towards general solution. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2862-2873, 2023.
Zhe Xu, Ruizhong Qiu, Yuzhong Chen, Huiyuan Chen, Xiran Fan, Menghai Pan, Zhichen Zeng, Mahashweta Das, and Hanghang Tong. Discrete-state continuous-time diffusion for graph gener- ation. In Advances in Neural Information Processing Systems, volume 37, 2024b.
Yuchen Yan, Yuzhong Chen, Huiyuan Chen, Minghua Xu, Mahashweta Das, Hao Yang, and Hang- hang Tong. From trainable negative depth to edge heterophily in graphs. In Advances in Neural Information Processing Systems, volume 36, 2024.
Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, and Danai Koutra. Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks. In 2022 IEEE International Conference on Data Mining, pp. 1287-1292. IEEE, 2022.
Hyunsik Yoo, Zhichen Zeng. Jian Kang, Ruizhong Qiu, David Zhou, Zhining Liu, Fei Wang, Charlie Xu, Eunice Chan, and Hanghang Tong. Ensuring user-side faimess in dynamic recommender systems. In Proceedings of the ACM on Web Conference 2024, pp. 3667-3678, 2024.
Hyunsik Yoo, SeongKu Kang, Ruizhong Qiu, Charlie Xu, Fei Wang, and Hanghang Tong. Embrac- ing plasticity: Balancing stability and plasticity in continual recommender systems. In Proceed- ings of the 48th International ACM SIGIR Conference on Research and Development in Informa- tion Retrieval, 2025a
Hyunsik Yoo, Ruizhong Qiu, Charlie Xu, Fei Wang, and Hanghang Tong. Generalizable recom- mender system during temporal popularity distribution shifts. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025b.
Ronghui You, Shuwei Yao, Hiroshi Mamitsuka, and Shanfeng Zhu. DeepGraphGO: Graph neural network for large-scale, multispecies protein function prediction. Bioinformatics, 37
(Supplement.))::262-2771 2021.
Qi Yu, Zhichen Zeng, Yuchen Yan, Lei Ying. Srikant, and Hanghang Tong. Joint optimal transport and embedding for network alignment. In Proceedings of the ACM on Web Conference 2025, pp- 2064-2075 2025.
Qi Yu, Zhichen Zeng, Yuchen Yan, Zhining Liu, Baoyu Jing, Ruizhong Qiu, Ariful Azad, and Hang- hang Tong. PlanetAlign: a comprehensive Python library for benchmarking network alignment. In The Fourfeenth International Conference Learning Representations, 2026.
17

---

Published as a conference paper at ICLR 2026
Zhichen Zeng, Si Zhang, Yinglong Xia, and Hanghang Tong. Parrot: Position- regularized optimal transport for network alignment. In Proceedings of the ACM web conference 2023, pp- 372-382, 2023a.
Zhichen Zeng, Ruike Zhu, Yinglong Xia, Hanqing Zeng, and Hanghang Tong. Generative graph dictionary learning. In Proceedings of the 40th International Conference on Machine Learning, pp. 40749-40769, 2023b.
Zhichen Zeng, Boxin Du, Si Zhang, Yinglong Xia, Zhining Liu, and Hanghang Tong. Hierarchical multi-marginall optimal transport for network alignment. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 16660-16668, 2024a.
Zhichen Zeng, Ruizhong Qiu, Zhe Xu, Zhining Liu, Yuchen Yan, Tianxin Wei, Lei Ying, Jingrui He, and Hanghang Tong. Graph mixup approximate Gromov-Wasserste geodesics. In Proceedings of the 41st International Conference on Machine Learning, 2024b.
Zhichen Zeng, Mengyue Hang, Xiaolong Liu, Xiaoyi Liu, Xiao Lin, Ruizhong Qiu, Tianxin Wei, Zhining Liu, Siyang Yuan, Chaofei Yang, Yiqun Liu, Hang Yin, Jiyan Yang. and Hanghang Tong. Hierarchical LoRA MoE for efficient CTR model scaling. arXiv preprint, 2025a.
Zhichen Zeng, Xiaolong Liu, Mengyue Hang. Xiaoyi Liu, Qinghai Zhou, Chaofei Yang, Yiqun Liu, Yichen Ruan, Laming Chen, Yuxin Chen, Yujia Hao, Jiaqi Xu, Jade Nie, Xi Liu, Buyun Zhang, Wei Wen, Siyang Yuan, Hang Yin, Xin Zhang, Kai Wang, Wen-Yen Chen, Yiping Han, Huayu Li, Chunzhi Yang, Bo Long. Philip S. Yu, and Jiyan Yang Hanghang Tong. InterFormer: Effective heterogeneous interaction learning for click-through rate prediction. In Proceedings of the 34th ACM International Conference Information and Knowledge Management, pp. 6225-6233, 2025b.
Zhichen Zeng, Wenxuan Bao, Xiao Lin, Ruizhong Qiu, Tianxin Wei, Xuying Ning, Yuchen Yan, Chen Luo, Monica Xiao Cheng, Jingrui He, and Hanghang Tong. Subspace alignment for vision- language model test-time adaptation. arXiv preprint, 2026a.
Zhichen Zeng. Ruizhong Qiu, Wenxuan Bao, Tianxin Wei, Xiao Lin, Yuchen Yan, Tarek F. Abdelza- her, Jiawei Han, and Hanghang Tong. Pave your own path: Graph gradual domain adaptation on fused Fromov-Wassersserrr geodesics. Transactions on Machine Learning Research, 2026b.
Zhichen Zeng, Qi Yu, Xiao Lin, Ruizhong Qiu, Xuying Ning, Tianxin Wei, Yuchen Yan, Jingrui He, and Hanghang Tong. Harnessing consistency for robust test-time LLM ensemble. Findings of EACL 2026, 2026c.
Acong Zhang, Ping Li, and Guanrong Chen. Steering graph neural networks with pinning control. arXiv preprint, 2023.
Yunkai Zhang, Qiang Zhang, Diji Yang, Ryan Lin, Ruizhong Qiu, Benyu Zhang. Hanchao Yu, Jason Liu, Yinglong Xia, Zhuokai Zhao, Lizhu Zhang, Xiangjun Fan, Zhuoran Yu, Abhishek Kumar, and Zeyu Zheng. Guiding generative recommender systems with structured human priors via heal decoding. In Proceedings of the ACM Web Conference 2026, 2026.
Xin Zheng, Yi Wang, Yixin Liu, Ming Li, Miao Zhang, Di Jin, Philip S. Yu, and Shirui Pan. Graph neural networks for graphs with heterophily: A survey. arXiv preprint, 2022.
Yilun Zheng, Xiang Li, Sitao Luan, Xiaojiang Peng, and Lihui Chen. Let your features tell the differences: Understanding graph convolution by feature splitting. In The Thirleenth International Conference on Learning Representations, 2025.
Duo Zhou, Yuji Zhang, Tianxin Wei, Ruizhong Qiu, Ke Yang, Xiao Lin, Cheng Qian, Jingrui He, Hanghang Tong, Heng Ji, and Huan Zhang. Geometric disentangelment unlearning. arXiv preprint, 2025.
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. Beyond homophily in graph neural networks: Current limitations and effective designs. In Advances in Neural Information Processing Systems, volume 33, pp. 7793-7804, 2020.
18

---

Published as a conference paper at ICLR 2026
Jiong Zhu, Yujun Yan, Mark Heimann, Lingxiao Zhao, Leman Akoglu, and Danai Koutra. Het- erophily and graph neural networks: Past, present and future. IEEE Data Engineering Bulletin, 2023.
Jiong Zhu, Gaotang Li, Yao-An Yang, Jing Zhu, Xuehao Cui, and Danai Koutra. On the impact of feature heterophily on link prediction with graph neural networks. arXiv preprint, 2024.
Jiaru Zou, Yikun Ban, Zihao Li, Yunzhe Qi, Ruizhong Qiu, Ling Yang, and Jingrui He. Transformer copilot: Learning from the mistake log LLM fine-tuning. In Advances in Neural Information Processing Systems 38, 2025a.
Jiaru Zou, Xiyuan Yang, Ruizhong Qiu, Gaotang Li, Katherine Tieu, Pan Lu, Ke Shen, Hanghang Tong. Yejin Choi, Jingrui He, James Zou, Mengdi Wang, and Ling Yang. Latent collaboration in multi-agent systems. arXiv preprint, 2025b.
19

---

Published as a conference paper at ICLR 2026
A EXPERIMENTAL SETTINGS (CONT'D)
A.1 DATASETS (CONT'D)
For heterophilic group, consider the following datasets, which widely used benchmarks for studying graph learning methods under heterophilic settings.
ACTOR (Pei al., 2020 ACTOR dataset an actor-only induced subgraph of the film dataset introduced by (Tang et al. 2009). The nodes are actors and the edges denote co- occurrence on the same Wikipedia page. The node features are keywords on the pages and we classify nodes into five categories.
Squirrel-Filtered (SQUIRREL-F, Platonov et al. 2023): SQUIRREL- is a page-page dataset. It is subset of the Wiki dataset (Rozemberezki et al. 202T) that focus the topic related to squirrel. Nodes are web pages and edges are mutual links between pages. The node features are important keywords in the pages and we classify nodes into five categories in terms of traffic of the webpage.
Caameleoon-Fitereee (CHAMELEON -F. Platonov et al. 2023): CHAMELEON-F is page- page dataset. It is subset of the Wiki dataset (Rozemberczki et al.) (021) that focus on the topic related to chameleon. Nodes are web pages and edges are mutual links between pages. The node features are important keywords in the pages and classify nodes into five categories in terms of traffic of the webpage.
MINESWEEPER (Platonov et al. 2023 MINESWEEPER is a synthetic dataset that simu- lates Minesweeper game with x 100 grid. Each node is connected to its neighboring nodes where 20% nodes are selected as mines at random. Node features are numbers of neighboring mines and the goal is to predict whether each test node is mine. These datasets are widely used as benchmarks for studying graph learning methods under heterophilic settings.
For the homophilic group, we consider the following datasets, which are standard homophilic net- work benchmarks.
CORA (Sen et al. 2008 Cora dataset is a citation network, where nodes represent sci- entific papers the machine learning field, and edges correspond citation relationships between these papers. Each node is associated with a set of features that describe the paper, represented as bag-of-wordd model. The task for this dataset is classify each paper into one of seven categories, reflecting the area of research the paper belongs to.
CITESEER (Sen et al., 2008): CiteSeer dataset is a citation network of scientific papers. consists of research papers as nodes, with citation links forming the edges between them. Each node is associated with a set of features derived from the paper's content, which is a bag-of-words representation of the paper's text. The task for this dataset is to elassify each paper into one of six categories, each representing a specific field of study.
A.2 BASELINE METHODS (CONT'D)
We briefly introduce
baseline methods as follows.
The first category is homophilic GNNs, which are originally designed under the homophily assump- tion.
ChebNet (Defferrard et al. 2016): Uses Chebyshev polynomials to approximate graph convolutions.
GCN (Kipf & Welling 2016): Employs first-order Chebyshev approximation for spectral graph convolutions.
SGC (Wu al., 2019); Simplifies GCN by removing non-linearities and collapsing weight matrices for efficiency.
GAT (Vehčković et al. 2018): Introduces attention mechanisms to assign adaptive impor-
tance to edges.
20

---

Published as a conference paper at ICLR 2026
GraphSAGE (Hamilton et al.
2017): Uses several aggregators for inductive graph learning.
GIN (Xu et al.
2018 Employs sum-based aggregation to maximize graph structure
pressiveness.
APPNP (Gsttegger et al. tion.
2019) Combines personalized PageRank with neural propaga-
GCNII (Chen et al.
2020): Extends GCN with residual connections and identity mapping
for deep GNN training.
GATv2 (Brody et 2021 Enhances GAT with dynamic attention coefficients for flexible neighbor weighting.
MixHop (Abu-El-Haija al. 2019): Aggregates multi-hop neighborhood features by mix- ing different powers of adjacency matrices.
TAGCN (Du al.
2017): Introduces trainable polynomial filters for adaptive, multi-scale
feature extraction.
DAGNN (Liu et al.
2020) Uses dual attention to decouple message aggregation and trans-
formation, improving depth scalability.
JKNet (Xu et al. (2018) Uses a jumping knowledge mechanism to combine features from different layers adaptively. We default the backbone GNN model to GCN.
Virtual Node Giimmr et al. p017); Introduces auxiliary global node to facilitate message passing. We default the backbone GNN model to GCN.
The second category heterophilic GNNs, which are designed for graphs where connected nodes often have different labels.
H2GCN (huu et al.
Enhances GNNs by ego-Ineighhor-emmeedingg seperation, higher-order neighbors and intermediate representation combinations.
2020
FSGNN (Maurya et al.
2021): : Employs soft feature selection and hop normalization over
GNN layers form a simple, shallow GNN. We use their default 3-hop variant.
ACM-GNN (Luan et al.
2022): Introduces adaptive channel mixing to diversify local in-
formation. We use their default ACM-GCN+ variant.
FAGCN (Bo et al. sentations.
2021): 1 Uses frequency adaptive filtering to learn optimal graph repre-
OrderedGNN (Song et al.
2023 : Aligns the order to encode neighborhood information
and avoids feature mixing.
GloGNN (Li et al.
2022): Incorporates global structural information to enhance graph
learning beyond local neighborhoods.
GGCN (Yan et al. 2022): Utilizes structure/feattrre-bsed edge correction to combat over- smoothing and heterophily.
GPRGNN (Chien et al. 2020): Introduces generalized PageRank propagation to capture the graph structure.
ALT (Xu al. 2023): Decomposes graph into components, extracts signals from these components, and adaptively integrate these signals.
The last category is graph transformers, which adapt transformer architectures graph data and look beyond local neighborhood aggregation.
NodeFormer (Wu al.
2022 ; Introduces all- all-pair message passing on layer-specific adap-
tive latent graphs, enabling global feature propagation with linear complexity.
SGFormer (Wu et al.
2024a): Develops a graph encoder backbone that efficiently computes all-pair interactions with lnyel attentive propagation.
DIFFormer (Wu et al. 2023): Proposes an mmergy-constraiin diffusion model, leading to variants that efficient and capable of capturing complex structures.
21

---

Published as a conference paper at ICLR 2026
A.3 TRAINING & EVALUATION (CONT'D)
For our method, we use wxx E {0.01, 0.1, 0.6, 8), wo E {0.1, 0.2, 0.5, 1J. E (0.01,0.1,17 1), and dropout rate 0.2. We use the GNN architecture described in the method section with 8 GNN lay- ers with hidden dimensionality 512 and add a two-layer MLP after each GNN layer for heterophilic graphs and use FAGCN for homophilic graphs. We use original node features as described in Sec- tion 3.2 except that we use zeros as the features of graph nodes on Squirrel-F and that we normalize the features of graph nodes on Cora and CiteSeer after computing the features of feature nodes. We train the GNN with learning rate 0.00003 for 1000 steps using the Adam optimizer (Kingma & Ba, 2014). Our method was implemented in PyTorch 2.7.0 and Deep Graph Library (DGL) 2.4.0, and experiments were run on Intel Xeon CPU @ 2.20GHz with 96GB memory and NVIDIA Tesla V100 32GB GPU.
B DEFINITION OF HOMOPHILY METRICS
To measure to what extent GRAPHITE can boost graph homophily on heterophlic datasets, we consider two popular homophily metries: feature homophily (Jin et al. 2022 and adjusted ho- mophily (Platonov et al. 2024). Formally, given a graph G. feature homophily feature is defined as follows:
H feature (g)
Σ (NS.D))EE
sim sim(
(13)
where sim, 02) == (X [Dry :] X [v,,;]] :I) is the cosine-similarity computed between features of nodes 02 Ug. This metric is a variant of the generalized edge homophily ratio Hodge proposed by (Jin
al. 2022), which measures the feature similarity between each of the connected node pairs in the graph dataset. Then, the adjusted homophily H Hadjusted is defined as follows:
Haajjsted (g) Hodde(g) De D (2/(218)22
(14)
1- --EC (2
where denotes the number of classes and Hodge (g) is edge homophily, which is defined similarly as Equation (13) with the similarity function sim(r;, 03) ty., -u, J. and
D=:= := Σ (l
(15)
NEV
is the sum of degrees deg(v) of nodes with label c, where Ye denotes the label of node Since we do not have node labels for the feature nodes when computing adjusted homophily, we assign them a "soft label," which is uniform probability distribution over the labels of its 1 1-hop neighbors.
C ADDITIONAL RELATED WORK
In modern machine learning research (Ma et al.
2022;
Yu al. 2026;
2025|
Zhang et al. 2026
2025 Chen et al. 2024 Wei et al. Chen al.
Bao et al.)
2026a
b 2025a b. 2024
2022
Cui et al. 2026
20266]
Liu et al. 2025a
b 2024a
2023
Bartan et al. 2025
Zeng et al.
2026a
2025a b 2024a b 2023a b Zou et
al.
2025a b
Lin et al. 2026 2025
2024 Zhou et al. 2025
Jing
al.
2025 Qiu al. 2026 2025a
2024
2023
2022 Xu et al.
2024b
Li al.]
2025a
20236
Yoo et al. 2025a
b 2024 Chan et al. 2024 Wu et al.) 2024b
He et al. 2026
2024
Wang
al.
2023). a problem related to heterophily is sqe-ssqussing. The over-squashing problem in
message passing neural networks arises when long-range information is exponentially compressed,
preventing effective dissemination across the graph (Alon & Yahav) 2020 (Sh et al., (2023)). pri-
mary research direction addresses this issue by identifying topological bottlenecks and modifying graph connectivity. Topping et al. (2021) established an initial framework linking oversquashing to graph Ricci curvature, demonstrating that negatively curved edges act as bottlenecks. Building on this idea, subsequent works have developed rewiring strategies inspired by curvature-basee princi- ples (Nguyen et al.) 2023 Shi al. 2023a). Beyond curvature, Black et al. (023) introduced perspective using effective resistance. Another line of research leverages spectral methods to coun- teract over-squashing, with notable approaches including spectral gaps RArraiz-Rodriguez et al.
22

---

Published as a conference paper at ICLR 2026
2022). expander graph constructions (Deac al. 2022), and first-order spectral rewiring (Karhad-
kar et al. 2022). More recently, Di Giovanni et al. (2023) provided a comprehensive analysis of
the factors contributing oversquashing. Additional solutions explore advanced rewiring strategies
and novel message-passee paradigms (Barbero et al. 2023 Qian et al. 2023 Beehrouz & Hashemi
2024
D THEORETICAL ANALYSIS
D.I ASSUMPTIONS
In this subsection, introduce the assumptions of theoretical analysis, which mild and realistic.
Given graph g (V,E, X) with & * Ø and X (0, 10,1) define the feature similarity
metric as sim UU) = X| [u = A X [1j, oo and use the feature homophily as the homophily metric:
hom (
Σ (HSN8))E
sim(u,, vz))
(16)
[8]
Furthermore, we assume that the original graph G is heterophilic. That is, we have hom(g) < while there exists pair of nodes, Us 0j (o% f vy). such that sim(v, 0ÿ) but (vt, U/) E.
Besides that, we assume that the given graph G does not have too dense features. Formally, we assume that | s O(V)) and that X llo O(EE). For the transformed graph G assume that every feature is used: for any feature k E X, there exists a graph node E > such that X [e, k] = 1.
D.2 TECHNICAL LEMMA
Here, prove technical lemma that will use later.
Lemma 4. Let A,B C R be nonempty, finite multisets such that
2.
A
B
ZEA
ZEB
Then,
Σ
B>>
Σ
AUB
A
SEALB
ZEA
Proof. To simplify notation, let
MA
1
Σ
2,
(17)
A
ZEA
1
AB
Σ
(18)
B
SEB
4
ILA
0.
(19)
23

---

Published as a conference paper at ICLR 2026
Then,
Σ
(20)
AUB
B
AA
REAUI
BEA
1
Σ
1
Σ
(21)
| +
|B|
|A|
BEA
#EB
BEA
[AA]
Σ SEA
2
1
Σ
(22)
[ +
B
A
[
BEB
SEA
(A4] MA +
Σ
1AA
(23)
A + BB]
ZEB
(14) ULA + [B] (B) - PA
(24)
|A| + [B]
(14) MAA + [3| MB (IAAI + [3] MA)
(25)
[A|
+ [B]
(18) 14 |B] MA
(26)
|A||
+ [B]
B
(uB - MA)
(27)
|
+
B
B
> O.
(28)
A
B
It follows that
Σ 2>
Σ ZEA
AUB
A
:EAL8
D.3 PROOF OF THEOREM 1
Homophily. Since the original graph 9 is homophilic, then there exists a pair of nodes, 2, 2 E V (v: # vz), such that sim(u,, Uy) = || [u,, :] A X [uj, :ll > 0 but (vi, 1y) & E. According to the definition of gt, we know that (v,, By) Et & 2, so Et E + Ø.
Furthermore, for any (12, U5) E gt \ E, since sim(u;, myy) = X [u;, :] A X [my. =]]l= >> 0. then there exists a feature & E X such that X [us, k) A [oj, k] > O. Since the feature matrix X is binary, then we must have
X [1%, k] =
X
(29)
follows that
sim(r;, u₁) = || (us. :] A X(r
(30)
max
X [Us, k'1 A X X(ey, *'1l
(31)
KEE
X [v:, A |]]
(32)
11 1
(33)
Since hg) < 1, then
sim(v,, U2) 2 1 > hom
(34)
Therefore, by Lemma with
A = {sim U5 03): (vi, E EB,
(35)
B [sim 0y): (v), E gt \E}
(36)
24

---

Published as a conference paper at ICLR 2026
have
1
hom(g')
Σ
(im Uz)
(37)
[
(FLRR))ESt
Σ
sim 4n)
(38)
E (ET
[E]
Σ
(39)
AA L B
SEALB
Σ
(40)
sim 0z)
(41)
(Rg.)))E = hg).
(42)
Number of edges. Since there are |V] nodes total, then the total number of node pairs is ((2)
Recall that Et \\ is the set of added edges. follows that
El - (M))
18t| |81 11t
(43)
MI(VI - 1) 0(1213)
(44)
2
D.4 PROOF OF OBSERVATION 2
Since sim U2 44) X [1, k] A X [oj,
=
X [D:y]] A :oo > 0, then there exists a feature k E X such that Since the feature matrix X is binary, then must have
X [ug, =, X X =.
(45)
This implies that (v,, 2x.) E Ed and that (u,, wx) E E*. Hence, there exists a length-2 path vs + I& byy connecting graph nodes v, and uy. Therefore, v and by two-hop neighbors of each other.
D.5 PROOF OF THEOREM S
Homophily. Since the original graph G is homophilic, then there exists pair of nodes, 8/ 1/ E V
(2; & 0z). such that sim(u; (Ug. v2y) |X [[ ]] A X ollooe > but (v,, 02) & E. Since sim 11)
[X i A X [[y,:Illo> >> 0, then there exists feature & E X such that X [u,, k] A X [2 k) > 0.
Since the feature matrix X is binary, then we must have
X k] 1, X X(u k] =.
(46)
This implies that (v,, Tx) E E" \ E and that (1y, mx) E E E. Thus, Ex \ E is nonempty.
Furthermore, for any feature node 2s E Vx. since any feature edge (1), E Ex ensures
((e,, k| = 1, then we have
X [0 kl =
1
Σ
X X k]
(47)
111 n (V x {x+))
1
Σ
1
(48)
Ex (V {xx))
1
Σ
(49)
Txx x (xx))
1.
(50)
25

---

Published as a conference paper at ICLR 2026
Finally, for any added feature edge (v,, 22x)) E Ex \ E Ex, sim (0,, mx)) = || [ua.:] A X [T -110
(51)
max | [Ua, k'] A X [TR,
(52)
E'E
2 X [us, Ri) A X (ox, k|]l
(53)
| = =1.
(54)
Since hom(g) < 1, then
sim T& 2 1 > hgm(
(55)
Therefore, by Lemma 4 with
A (sim( my) (0,, 04)) EB.
(56)
B = (sim(er, IX): (0,, E Ex Br
(57)
have
hom (9*)
Σ
sim(u, n²)
(58)
|841
(u,"")E
sim(u, u')
(59)
E [11E11
(u.n))E Ex
1
A
B|
2
(60)
ZEALIB
=
(61)
ZEA
1
Σ
sim v2))
(62)
(D.,E))EE
hom)
(63)
Number of nodes. Since | S O(VV)) then
|x1 = |x|| - 0()).
(64)
It follows that
|-1 = | + |Vxl
(65)
- |I + O(V)
(66)
= =O(VV).
(67)
Number of edges. Since X is a binary matrix, then || | = |X S O(E)). Hence,
E11|= = Σ Σ [(),,))EEE
(68)
Σ
(69)
VEV VEVREX KEX
Σ
[[x[v,A]=1|
(70)
NEV
Σ
Σ
X
[vs, k]
(71)
KEV
Σ Σ
X (1 k||
(72)
XEX
= ||X |11 = X llu S O(E)).
(73)
It follows that
|-*|| = |3 + |11l
(74)
$ | + +((E))
(75)
((18)).
(76)
26

---

Published as a conference paper at ICLR 2026
E USE OF LARGE LANGUAGE MODELS
We made limited and controlled use large language models (LLMs) solely for stylistic refinement and improving readability of the text. All scientific content, methodology, experiments, and conclu- sions were fully conceived and validated by the authors. The role of LLMs was purely editorial and does constitute co-authorship.
27


Reviews:
[{"review_id": "Thu3tuHIN8", "reviewer": "Reviewer_3mQE", "summary": "This paper introduces GRAPHITE, a novel graph transformation framework that addresses heterophily in GNNs by explicitly increasing graph homophily through the creation of feature nodes. Unlike existing methods that focus on architectural modifications, GRAPHITE directly transforms the graph structure by introducing feature nodes that serve as hubs to connect nodes with similar discrete features. The method provides theoretical guarantees for homophily improvement with minimal graph size increase and demonstrates strong empirical performance across multiple heterophilic benchmarks, outperforming state-of-the-art methods while maintaining competitive performance on homophilic graphs.", "questions": "1. How can GRAPHITE be extended to handle continuous node features while maintaining theoretical guarantees?\n\n2. What is the impact of the transformation on graph structural properties beyond homophily, and could certain properties be adversely affected?\n\n3. Have the authors considered alternative feature node representations beyond averaging, such as learned embeddings or attention-weighted aggregations?\n\n4. How does the method scale to extremely large graphs in practice, considering the increased node count from feature node addition?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. First work to explicitly transform graph structure to increase homophily, offering a fundamentally different approach from architectural GNN modifications.\n2. Provides formal proofs showing GRAPHITE guarantees homophily improvement (Theorem 3) with only linear growth in graph size.\n3. Consistently outperforms 25 baselines across 4 heterophilic datasets with improvements up to 5.35%, while maintaining competitive performance on homophilic graphs.", "weaknesses": "1. Method is specifically designed for discrete node features, limiting applicability to graphs with continuous features without discretization.\n2. While theoretical complexity is linear, practical implementation with 8 GNN layers and 512 hidden dimensions may be computationally expensive for very large graphs.\n3. Feature node representations (Equation 5) use simple averaging, potentially overlooking more sophisticated feature aggregation strategies.\n4. No exploration of how the transformation affects graph properties like diameter, clustering coefficients, or other structural metrics beyond homophily.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "iAztyLjKs5", "reviewer": "Reviewer_3w7g", "summary": "This paper introduces the GRAPHITE method, which improves the performance of Graph Neural Networks (GNNs) on heterophilic graphs by introducing feature nodes to increase the graph's homophily. Unlike existing approaches, GRAPHITE directly enhances homophily through graph transformation rather than architectural changes. The method is theoretically proven to be effective and is validated through extensive experiments, demonstrating significant improvements on heterophilic datasets. Results show that GRAPHITE outperforms state-of-the-art methods on heterophilic graphs while remaining competitive on homophilic graphs.", "questions": "1、The paper repeatedly claims to be the first to propose a graph transformation method for improving performance on heterophilic datasets. However, there are numerous prior works, such as those by [1] and [2], that have also modified graph structures by introducing virtual nodes to enhance performance on heterophilic graphs. Unfortunately, the paper does not adequately discuss or compare these related studies\n\n[1]Dong Y, Dupty M H, Deng L, et al. Differentiable cluster graph neural network. ICLR 2025.\n[2]Zhang A, Li P, Chen G. Steering graph neural networks with pinning control[J]. arXiv preprint arXiv:2303.01265, 2023.\n\n\n2、The feature of a feature node is defined $V_{x}$ as the \" we define its node feature as the average feature vector among the graph nodes $v_i$, that are connected to feature node $x_k$:\" (Equation 5). However, the rationale behind this design has not been sufficiently justified. If the features of the graph nodes $v_i$, connected to the same feature node $x_k$ exhibit significant differences (for example, some nodes may share the feature k while having completely distinct other features), the average feature may not effectively represent the feature node and could even introduce noise. Has the author compared the effectiveness of other feature construction methods for characteristic nodes, such as majority voting or weighted averaging? Why has \"mean\" been selected as the optimal choice?\n\n3、The ablation experiment did not analyze the sensitivity of hyperparameters such as \"feature edge weights w_x\" and \"temperature parameter τ\" to performance. Do the values of these parameters have significant dataset dependencies? If the dataset is changed, is the cost of hyperparameter tuning too high?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1)\tIntroduces a graph transformation method that effectively boosts homophily on heterophilic graphs.\n2)\tBoth theoretical analysis and empirical experiments support the superior performance of GRAPHITE on heterophilic graphs.\n3)\tDemonstrates good compatibility and enhancement effects with existing GNN architectures.", "weaknesses": "1)\tLimited Novelty in Core Idea: While the paper introduces the GRAPHITE method to boost homophily in heterophilic graphs, the fundamental concept of adding feature nodes or shortcut connections between similar nodes is not entirely novel. Many prior works have explored similar strategies for improving graph neural network (GNN) performance, such as feature augmentation or graph transformation methods. The core idea lacks significant innovation, which might reduce its contribution to the broader research community.\n\n2)\tScalability Concerns: Although the paper claims that the proposed method is computationally efficient, it does not provide a detailed analysis of how GRAPHITE performs on very large-scale graphs. The addition of feature nodes and edges might still become a bottleneck in cases with millions of nodes, limiting the method's practical applicability for large datasets. More empirical evaluation on large graphs or an analysis of the method’s time complexity would be valuable.\n\n3)\tNarrow Focus on Discrete Features: The proposed method mainly focuses on graphs with discrete node features. However, many real-world applications (e.g., social networks, biological networks) involve continuous features. The paper does not address how GRAPHITE would perform in such scenarios or whether the approach could be generalized to handle graphs with continuous or mixed-type features. This limits the generalizability of the method to broader domains.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "dToBm8LKgF", "reviewer": "Reviewer_6ZKr", "summary": "This paper proposes GRAPHITE (GRAph homoPHIly boosTEr), a novel framework that directly enhances graph homophily on heterophilic graphs through an explicit structural transformation. Unlike prior methods that rely on architectural modifications, GRAPHITE introduces “feature nodes” as intermediaries to indirectly connect original nodes sharing similar discrete features—significantly boosting homophily with only a modest increase in graph size. The authors provide theoretical guarantees for this homophily gain and demonstrate through extensive experiments that GRAPHITE substantially outperforms state-of-the-art methods on heterophilic benchmarks, while also improving the performance of standard homophilic GNNs when applied to the transformed graph.", "questions": "See Weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Paradigm shift:  GRAPHITE directly enhances graph homophily through structural transformation, offering a fundamental and effective new perspective. Moreover, its design is simple and efficient: by using feature nodes as hubs, it avoids the O(|V|²) edge explosion and achieves provably improved homophily with only O(|V|) added nodes and O(|E|) added edges.\n\n2. It elegantly decouples feature similarity from the original graph structure to enable semantics-aware message passing: by introducing feature nodes that explicitly connect nodes sharing discrete features, GRAPHITE preserves the original topology while adding semantic similarity-based propagation paths—allowing GNNs to naturally integrate structural and semantic homophilic signals without architectural changes.\n\n3. Comprehensive and convincing experiments: GRAPHITE consistently outperforms more than 25 state-of-the-art methods across four heterophilic datasets and significantly boosts the performance of standard homophilic GNNs when applied to the transformed graph.", "weaknesses": "1. Although the authors list the hyperparameter search ranges and training configurations in Section 4.1 and Appendix B.3, the paper lacks sensitivity analysis of these parameters, which weakens the robustness argument of the method.\n\n2. The homophily definition used in the theoretical analysis (based on feature intersection) is inconsistent with the experimental metrics (feature/adjusted homophily). Although the two are intuitively positively correlated, the paper lacks rigorous derivation or empirical validation—e.g., is the theoretical homophily strongly correlated with the experimental homophily metrics across all datasets?\n\n3. Feature nodes only connect nodes that share the same discrete features, which may cause the GNN to aggregate neighbors based on \"shared keywords\" rather than \"same-class labels.\" Can case studies or ablation experiments demonstrate that feature nodes indeed promote semantic or label-level homophily, rather than merely feature-level aggregation?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "aOnEU1JAKm", "reviewer": "Reviewer_aJfv", "summary": "This paper proposes GRAPHITE, a simple graph transformation that explicitly increases homophily before running any GNN: it adds a set of feature nodes (hubs) and connects each original node to the hubs matching its discrete features, so that nodes with similar features become two hops apart and message passing becomes homophilic.", "questions": "1. It is found that higher homophily does not always indicate a better graph for GNNs, i.e. mid-homophily pitfall [1]. How does the motivation of your proposed homophily booster align with this conclusion?\n\n2. More experiments on large scale datasets used in [2].\n\n3. What is the motivation or intuition behind the definition of shortcut connection, which connect nodes that share at least one feature dimension?\n\n4. Naive homophily booster (NHB) oversimplifies the distribution of features. As shown in [3], there exist both good and bad features for message passing, and if you connect two nodes when they share a bad feature, you will not obtain a graph with higher homophily.\n\n5. To my understanding, the number of feature nodes is the same as the number of feature dimension. For the tasks they have very large feature dimensions, this will significantly increase the number of nodes in the graph. What is the computational complexity of your algorithm?\n\n6. Will the connection to \"hub” nodes cause over-squashing problem?\n\n7. Missing baselines in your comparison, e.g. ACMGNN [4], FSGNN [5]\n\n\n[1] When do graph neural networks help with node classification? investigating the homophily principle on node distinguishability. Advances in Neural Information Processing Systems. 2024 Feb 13;36.\n\n[2] Finding global homophily in graph neural networks when meeting heterophily. In International Conference on Machine Learning, pp. 13242–13256. PMLR, 2022.\n\n[3] Let Your Features Tell The Differences: Understanding Graph Convolution By Feature Splitting. InThe Thirteenth International Conference on Learning Representations.\n\n\n[4] Revisiting heterophily for graph neural networks. Advances in neural information processing systems. 2022 Dec 6;35:1362-75.\n\n\n[5] Simplifying approach to node classification in graph neural networks. Journal of Computational Science, 62, 101695.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The motivation is straightforwad and the presentation is easy to understand.", "weaknesses": "Some motivation and explanation need more solid justifications.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:

Decision: Accept (Poster)