On the Benefits of Attribute-Driven Graph Domain Adaptation
Authors: Ruiyi Fang
Bingheng Li
zhao kang
Qiuhao Zeng
Nima Hosseini Dashtbayaz
Ruizhi Pu
Boyu Wang
Charles Ling
Venue: ICLR 2025 Poster (2025)
Keywords: Deep Learning and representational learning
Primary area: transfer learning, meta learning, and lifelong learning

Abstract: Graph Domain Adaptation (GDA) addresses a pressing challenge in cross-network learning, particularly pertinent due to the absence of labeled data in real-world graph datasets. Recent studies attempted to learn domain invariant representations by eliminating structural shifts between graphs. In this work, we show that existing methodologies have overlooked the significance of the graph node attribute, a pivotal factor for graph domain alignment. 
Specifically, we first reveal the impact of node attributes for GDA by theoretically proving that in addition to the graph structural divergence between the domains, the node attribute discrepancy also plays a critical role in GDA. Moreover, we also empirically show that the attribute shift is more substantial than the topology shift, which further underscore the importance of node attribute alignment in GDA. Inspired by this finding, a novel cross-channel module is developed to fuse and align both views between the source and target graphs for GDA. Experimental results on a variety of benchmark verify the effectiveness of our method.

Published as a conference paper at ICLR 2025
ON THE BENEFITS OF ArTRIBUTE-DRIVER GRAPH Do- MAIN ADAPTATION
Ruiyi Fang Bingheng Li Zhao Kang*
Qiuhao Zeng'
Nima Hosseini Dashtbayaz'
Ruizhi Pu
Boyu Wang Charles Ling!.3 1.3
Western University Michigan State University
Vector Institute
University of Electronic Science and Technology of China
[rfang32, rpu2, qzeng53, charles .ess.inng ling @uwo. ca 1 Libinghe@mnn @msu. edu zkangluestc. edu. cn bwanggcsd. uwo. ca
ABSTRACT
Graph Domain Adaptation (GDA) addresses a pressing challenge in cross-network learning, particularly pertinent due to the absence of labeled data in real-world graph datasets. Recent studies attempted to learn domain invariant representations by eliminating structural shifts between graphs. In this work, we show that existing methodologies have overlooked the significance of the graph node attribute, a pivotal factor for graph domain alignment. Specifically, we first reveal the impact node attributes for GDA by theoretically proving that in addition to the graph structural divergence between the domains, the node attribute discrepancy also plays a critical role in GDA. Moreover, we also empirically show that the attribute shift is more substantial than the topology shift, which further underscore the importance of node attribute alignment in GDA. Inspired by this finding, a novel cross-channel module developed to fuse and align both views between the source and target graphs for GDA. Experimental results on a variety of benchmark verify the effectiveness of our method.
INTRODUCTION
In the area of widespread internet data collection, graph vertices are frequently associated with content information, referred as node attributes within basic graph data. Such graph data can be widely used in prevalent real-world applications, with data suffering from label scarcity problems in annotating complex structured data is both expensive and difficult (Xu et al., 2022). To solve such a challenge, transferring abundant labeling knowledge from task-related graphs is a method considered (Chen al., 2019). Giving labeled graphs as a source to solve unlabeled graph targets has been proposed as graph domain adaptation (GDA) as a paradigm to effectively transfer knowledge across graphs by addressing distribution shifts (Shi et al., 2024).
Early works GDA apply deep domain adaptation (DA) techniques directly, thereby (Shen et al., 2020b: Shui et al., 2023; Wu al., 2020; Shen al., 2020a; Dai et al., 2022) without considering the topologi- cal structures of graphs for domain alignment. To address this issue, several recent works have been proposed to leverage the inherent properties of graph topology (e.g., adjacency matrix). While these meth- ods (Yan & Wang. 2020; Shi et al., 2023; Shen et al., 2023: Wu et al., 2023) have achieved substantial im- provements by alleviating the topological discrepancy between domains, they overlook the importance of node attributes, fundamental aspect of GDA. To verify our argument, we investigate the projected feature values? of graph topology and attribute on
0.00A
Avergga
Sigelagy
Topalogy
Worilaut
#ACM
DOLP
Chosserr
USSA
Bruzil
#uurpe
Figure 1: This represents feature value in two groups of datasets. This shows the feature value distribution gap in the attribute is larger than in the topology.
" "Equal contribution 1 'Corresponing author
Details on the construction of project features are presented in SectionB of Appendix.

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Published as a conference paper at ICLR 2025
two GDA benchmarks, as shown in Figurel. It can be observed that feature value discrepancy exists in all GDA benchmark inside datasets, with feature value discrepancy for attributes significantly larger than topology feature value discrepancy. Based this observation, graph distribution shift exists in both attribute and topology: (2) attribute divergence between the source and target graphs is more significant than the topology divergence.
conclude that (1)
Source Graph
(i) Before Alignment
Freelagy Gape
GCN Layer
2
4
S
Source
Guoiifer
+
AV
GCN Layer
(i) Exis Existing Methods
Domain Loss Lp
Shared Przameters
Dorrais Clareleer
Target Graph
Attribute Driven Alignment
(ii) GAA
C7
GCN Layer
Target Loss by
GCN Layer
27
Source Target
(a) Framework Overview
(b) Attribute-Driven Example
Figure 2: (a) An overview of our method. GAA gives attribute and topology graph representation, where minimizing source and target distribution shift through two views. (b)(i) Distribution shifts exist both topology and attribute views before alignment. (ii) Existing GDA algorithms can only address graph topology shifts but not attribute shifts. (iii) GAA can address GDA attribute shifts.
Motivated by this observation, we theoretically investigate the domain discrepancy between two graphs, revealing the role of node attribute for GDA. Specifically, by leveraging the PAC-Bayes framework, we derive a generalization bound of GDA, which unveils how graph structure and node attributes jointly affect the expected risk of the target graph. Moreover, we also show that the discrepancy between the source and target graphs can be upper bounded in terms of both node attributes and topological structure. other words, our theoretical analysis reveals that both attribute and topology views should be considered for GDA, with the former having a more significant impact on domain alignment, as shown in Figure
Our theoretical insights highlight the significance of characterizing the cross-network domain shifts in both node attributes and topology. To this end, we propose a novel cross-channel graph attribute- driven alignment (GAA) algorithm for cross-network node classification, (a). Unlike existing methods that rely solely on topology, GAA also constructs an attribute graph (feature graph) to mitigate domain discrepancies. Furthermore, GAA also introduces cross-view similarity matrix, which acts as a filter to enhance and integrate feature information within each domain, facilitating synergistic refinement of both attribute and topology views for GDA. Figure2 (b) illustrates the benefits of GAA for GDA, which alleviates both attribute and topology shifts.
shown in Figure.2 .
Our main contributions summarized as follows:
We reveal the importance of node attributes in GDA from both empirical and theoretical aspects.
Motivated by our theoretical analysis. we proposed GAA, novel GDA algorithm that minimizes both attribute and topology distribution shifts based on intrinsic graph property.
Comprehensive experiments on benchmarks show the superior performance of our method compared to other state-of-thh--n methods for real-world datasets of the cross- network node classification tasks.
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2 RELATED WORK
Unsupervised domain adaptation is a wildly used setting of transfer learning methods that aims to minimize the discrepancy between the source and target domains. To solve cross-domain classification tasks, these methods are based on deep feature representation (Zhu et al., 2022; Zeng et al., 2024b;a), which maps different domains into a common feature space, Some recent studies have overcome the imbalance of domains and the label distribution shift of classes to transfer model well (Zeng et al., 2023; Jing et al., 2021: Zeng et al., 2025; Xu al., 2023; Pu et al., 2025; Zeng et al., 2024c). Some novel settings in domain adaption have also gotten lot of attention, like source free domain adaption(SFDA) (Yang et al.. 2021; Xu et al., 2025), test time domain adaption(TTDA) (Wang et al., 2022). As for graph-structured data, several studies have been proposed for cross-graph knowledge transfer via GDA setting methods (Shen & Chung, 2019; Dai et al., 2022; Shi et al., 2024). ACDNE (Shen et al., 2020a) adopt k-hop PPMI matrix to capture high-order proximity as global consistency for source information on graphs. CDNE (Shen et al., 2020b) learning cross-network embedding from source and target data to minimize the maximum mean discrepancy (MMD) directly. GraphAE (Yan & Wang, 2020) analyzes node degree distribution shift in domain discrepancy and solves it by aligning cressage-passinggg routers. DM-GNN (Shen et al., 2023) proposes method to propagate node label information by combining its own and neighbors' edge structure. UDAGCN (Wu al., 2020) develops dual graph convolutional network by jointly capturing knowledge from local and global levels to adapt it by adversarial training. ASN (Zhang et al., 2021) separates domain- specific and Nomain-invariaat variables by designing a private en-coder and uses the domain-specific features in the network to extract the Nomain--nvariann shared features across networks. SOGA (Mao et al., 2024) first time uses discriminability by encouraging the structural consistencies between target nodes in the same class for the SFDA in the graph. GraphAE (Guo et al., 2022) focuses on how shifts in node degree distribution affect node embeddings by minimizing the discrepancy between router embedding to eliminate structural shifts. SpecReg (You et al., 2022) used the optimal transport- based GDA bound for graph data and discovered that revising the GNNs' Lipschitz constant can be achieved by spectral smoothness and maximum frequency response. JHGDA (Shi et al., 2023) studies the shifts in hierarchical graph structures, which are inherent properties of graphs by aggregating domain discrepancy from all hierarchy levels to derive comprehensive discrepancy measurement. ALEX (Yuan et al., 2023) first creates label shift enhanced augmented graph view using a low- rank adjacency matrix obtained through singular value decomposition by driving contrasting loss. SGDA (Qiao et al., 2023) enhances original source graphs by integrating trainable perturbations (adaptive shift parameters) into embeddings by conducting adversarial learning to simultaneously train both the graph encoder and perturbations, to minimize marginal shifts.
3 THEORETICAL ANALYSIS
In this subsection, we provide a discussion on the FAC-Bayesian analysis with the graph domain adaptation.
Notations. An undirected graph G = A, consists of a set of nodes V and edges E, along with adjacency matrix A, feature matrix X, and a label matrix Y. The adjacency matrix A E NxN encodes the connections between N nodes, where Ag = indicates an edge between nodes i and j, and Aij = 0 means the nodes are not connected. The feature matrix X E Nxx represents the node features, with each node described by d-dimensional feature vector. Finally, Y E NC contains the labels for the N nodes, where each node is classified into one of C classes.
In this work, we explore the task of node classification in a unsupervised setting, where both the node feature matrix X and the graph structure A are given before learning. We assume that all key aspects of our analysis are conditioned on the fixed graph structure A and feature matrix X. while the uncertainty arises from the node labels Y. Specifically, we assumee that the label y, for each node i E V drawn from a latent conditional distribution Pr(ya Z,),, where Z = s(X,C) G), with ʃ being an aggregation function that combines features from the local neighborhood of each node within the graph. Additionally, we assume that the labels for different nodes are independent of each other, given their respective aggregated feature representations Z,. With partially labeled node set Vo S V. our objective in the node classification problem is to learn a model h : RR Nxd xd x GN + N from family of classifiers 7 that can predict the labels for the remaining unlabeled nodes. For given classifier h, the predicted label Y for node i is determined by: y; arg max h (X,
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where (X, G) is the output corresponding to node i and (2 [) represents the score for the k-th class for node i.
Margin loss each domain. Now can define the empirical and expected margin loss of
a classifier E H. on source graph G = (V$,E* , X y and target graph G"
,
{v= ,ET,, , {²). Given Y the empirical margin loss of h on G for a margin 2 0 is defined as
(g(h) = Ns iEVS
E1
Σ
[mxxx, / s ma (an,,sss) No ea
(I)
where 1 [+] is the indicator function, e represents node labeling. The expected margin loss is then defined as
(g) By,, Px.,,
Definition 1 (Expected Loss Discrepancy). Given a distribution P over a function family H. for any À > 0 and y 2 0, for any G and G define the expected loss discrepancy between V and VL as A) == In ERPpe X( ( (h) (3)-)3(h)) where (h) and (g(h) follow the definition of Eq. (2).
Intuitively, (P; captures the difference the expected loss between V and D"r in an average sense (over P).
Theorem (Domain Adaptation Bound for Deterministic Classifiers). Let H be a family of clas- sification functions. For any classifier h in H. and for any parameters X > O and x 2 0, consider any prior distribution over H that independent of the training data VS. With probability of at least 1 - over the sample Y5, for any distribution Q on H such that 2. the following inequality holds:
Pr maxie G)) h,(X, < 3]
>
2 + 1 2(DKL(Q11P) + +1
X 4Ns
+ D3P((:A))
We follow the characterization from (Ma al., 2021). In the generalization bound, the KL--divergence DKL(Q1/2 is usually considered as a measurement of the model complexity. The terms In and TN₂ commonly seen in MAC-Bayesian analysis for IID supervised settings. The expected loss discrepancy D5 (P:A)) between the source nodes V and the targeted nodes yL is essential to our analysis. To derive the generalization guarantee, need upper- upper-bound the expected loss discrepancy D' D3... (P)
Proposition (Bound for D3;((P;)) For any 7 2 O, and under the assumption that the prior distribution P over the classification function family H. is defined, we establish bound for the domain discrepancy measure D P.;,, >). Specifically, we have the following inequality:
D3 50
Σ LEVS JEVE
Σ Σ - iEV8 JEVT
(4)
From Proposition 1, the topological divergence 1 ([(A==))-(AA'x*? x²) and the attribute divergence ||X3 -x/111 constitute the upper bound of D3 (P: (P: and further bound graph domain discrepancy. It also reveals both attribute and topology divergence of intrinsic graph property influence on GDA generalization
We introduce attribute and topology alignment loss by minimizing attribute divergence by utilizing the attribute graph and minimizing topology divergence by utilizing the original graph. Graph G and GT with topology information A and A" through the feature extraction module represent (AS X5); and (AS X3)
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4 THE PROPOSED METHODOLOGY
In this section, we propose a novel GDA method with attribute-driven alignment (GAA), which first minimizes graph attribute divergence. The overall framework of GAA is shown Figure2. The main components of the proposed method include the specific attribute convolution module and the attribute-driven alignment module. We will detail the proposed GAA in the following subsections.
4.1 SPECIFIC ATTRIBUTE CONVOLUTION MODULE
Inspired by Proposition we design an attribute-driven GDA model by using topology graph and feature graph. Our model mainly contains attribute-driven alignment that directly minimize discrimination in attribute and topology between source and target graph.
Feature Graph Merely using node attribute information through X is unstable (Fang et al., 2022; Mao al., 2023; Li al., 2024; Xie al., 2024b). A natural idea would be utilize graph node attribute by fully making use of the information through feature space propagation (Wang et al., 2020; Xie et al., 2024a; Kang al., 2024; Li al., 2025). Therefore we introduce feature graph into our work.
To represent the structure of nodes the feature space, we build a ANN graph & based on the feature matrix X. To be precise, node similarity matrix SM is computed using the cosine similarity formula:
X X3
SM
(5)
Xx, |X,
where SMaj is the similarity between node feature X, and node feature Xj. We derivate feature graph G = E, X,Y] which shares the same X with G, but has a different adjacency matrix. Therefore, topology graph and feature graph refer to G and & respectively. Then for each node we choose the top k nearest neighbors and establish edges. this way, we construct feature graph in attribute view for the source graph ČS = {{Vs ES AS X y and target graph
GT -T AT AT, }T)
Feature Extraction Module To extract meaningful features from graphs. we adopt GCN that is comprised of multiple graph convolutional layers. With the input graph G. the (1 + 1)-th layer's output (1+1) can be represented as:
H(++ = ReLU
ADD - ((OW))
(6)
where ReLU is the Relu activation function (-eLU(-) maz(0. )), D is the degree matrix of A. W!l) is layer-specific trainable weight matrix, H!) is the activation matrix in the i-th layer and H = X. In our study we use two GCNs to exploit the information in topology and feature space. For source graph, output node embedding is donated by 2 generated from G and Z generated from ÕS Similarly, for the target graph, the output node embedding is donated by 7" generated from GT and z7. generated from GT.
4.2 SOURCE CLASSIFIER LOss
The source classifier loss Ls (fs (Z5). S)) is to minimize the cross-entropy for the labeled data node in the source domain:
N₂ Σ o log (0.")
Ls (fs xz) Ns
where od denotes the label of the i-th node in the source domain and 0 are the classification prediction for the i-th source graph labeled node oz E ys.
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4.3 ATTRIBUTE-DRIVER ALIGNMENT
To make the attribute view fully learnable, we design the attention attribute module dynamically utilize the important attribute. Specifically, we design learnable domain adaptive models for alignment embeddings in topology and attribute views.
Attention-baan based Attribute To guide the network to take more attention to the important node attributes and make attributes learnable, we design attention-based embedding models. Specifically, we map the node attributes into three different latent spaces. By given an example in source graph Wi M W.Zs", where WG E Raxd W Rdxd W E Rgdxd are the learnable parameter matrices. And Q E RuxN, K E RaxN and M E RaxN denotes the query matrix, key matrix and value matrix, respectively.
attribute embedding: Q W W
The attention-based attribute matrix atty can be calculated by:
(KTQ)
att softmar
M
(8)
Vd
Likewise, we can obtain a similar objective of each learnable graph embedding att att and att?
Cross-view Similarity Matrix Refinement
Subsequently, the view similarity matrix S represents the similarity between the source attribute and topology graph. 5 represents the similarity between the target attribute and topology graph. S as formulated:
2 (ZS)
S
(9)
| 2 1|
Likewise, we can obtain a similarity matrix the target graph by:
2 2 (ZT)
ST
(10)
127 1|T
where S and 8 is the cross-view similarity matrix, and (-) the function calculate similarity. Here, we adopt cosine similarity Qian et al. (2024). The proposed similarity matrix S and S measures the similarity between samples by comprehensively considering attribute and structure information. The connected relationships between different nodes could be reflected by S$ and ST. Therefore, we utilize 5d and S to refine the structure in augmented view with Hadamard product, all can be formulated as:
all all S
(11)
Similarly we can get a by att SS att? by att" O ST att? by att" ST. which respectively represent source graph and target graph in both topology view and attribute view embedding.
AttributeDrriven Domain Adaptive
The proposed framework follows the transfer learning paradigm, where the model minimizes the divergence of the two views. In detail, GAA jointly optimizes two views of GDA alignment. To be specific, C is the Mean Squared Error (MSE) loss between the source graph all and all and the target graph att? and att?, which can be formulated as:
CA - (Ilatt -att" I1 |latt att f
(12)
We adapt the domain in two views, domain classifier loss in the topology view ||att5 att? 1
enforces that the attribute graph node representation after the node feature extraction and similarity matrix refinement from source and target graph G and Gf. Similarly, we get ||att att

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from G and G², And | att" corresponds to the first item of Proposition 1, which is ): X4)
means minimizing structural distribution shift. In attribute view is
||att - attyy 1 trying to discriminate corresponds to the second term |X X316 22 of Proposition 1. which means minimizing attribute distribution shift.
4.4 TARGET NODE CLASSIFICATION
We use Gradient Reversal Layer (GRL) (Ganin et al., 2016) for adversarial training. Mathematically, we define the GRL as Qx( with reversal gradient [Qx(²) -XL Learning a GRL adversarial in such way that: the side, the reversal gradient enforces fs( to be
maximized; on the other side, 0p is optimized by minimizing the cross-entropy domain classifier
loss:
Ns
CD = Ns NT
Σ
m. log (m,) + mm)) log (1 mm))
(13)
where m, E {0, 1] denotes the groundtruth, and n. denotes the domain prediction for the i-th node in the source domain and target domain, respectively. To utilize the data in the target domain, we use entropy loss for the target classifier fr:
NT Σ log (it)
Cr (fr (z²))
(14)
NT
where of¹ are the classification prediction for the i-th node the target graph of. Finally, by combining LAs Lis, LD and fr, the overall loss function of model can be represented as:
f = LA aLs 3LD +TC
(15)
where a, and are trade-off hyper- The parameters of the whole framework are updated via backpropagation.
5
EXPERIMENT
5.1 DATASETS
To prove the superiority of our work domain adaptation node classification tasks, we evaluate on four types of datasets, including Airport dataset (Ribeiro et al., 2017), Citation dataset (Wu al., 2020), Social dataset (Liu et al., 2024a) and Blog dataset (Li et al., 2015). The airport dataset involves three countries' airport traffic networks: USA (U), Brazil (B), and Europe (E), in which the node indicates the airport and the edge indi- cates the routes between two airports. The citation dataset includes three different citation networks: DBLPv8 (D). ACMv9 (A), and Citationy2 (C), in which the node indicates the article and the edge indicates the citation relation between two articles. As for social networks, we choose Twitch gamer networks and Blog Network, which are collected from Germany(DE) and England(EN). Two disjoint Blog social networks, Blogl (B1) and Blog2 (B2), which are extracted from the BlogCatalog dataset. extracted from the BlogCatalog dataset. Because these four groups of dataset ingredients gener- ated from different data sources, their distributions naturally diverse. For comprehensive overview of these datasets, please refer to Tab 5.
Types
Datasets
#Node
#Edgee
#Labbll
USA
1,190
13,599
Airport
Brazil
131
1,038
4
Europe
399
5,995
ACMv9
9,360
15,556
Citation
Citationv
8,935
15,098
5
DBLPv7
5,484
8,117
Blog1 Blog2
2,300
33,471
Social
6
2,896
53,836
Germany
9,498
153,138
Social
England
7,126
35,324
US
132,558
697.450
CN
101,952
285,561
DE
43,032
126.683
MAG
JP 37.498
90,944
20
RU 32,833
67. (7.994
FR 29,262
78.222
Table 1: Dataset Statistics.
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Methods
U-+
U
E
E B B
DE
EN
EN
DE
GCN
0.366
0.371
0,491
0.452
0,439
0.298
0.673
0.634
ENN-GGN
0.436
0,437
0.461
0.478
0.459
0,464
0.661
0.623
DANN
0.501
0,386
0.402
0.350
0.436
0.538
0.512
0.528
DANE
0.531
0.472
0.491
0.489
0.461
0.520
0.642
0.644
UDAGCN
0.607
0.488
0.497
0.510
0.434
0.477
0.724
0.660
ASN
0.519
0.469
0.498
0.494
0.466
0.595
0.550
0.679
EGI
0,523
0.451
0.417
0.454
0.452
0.588
0.681
0.589
GRADE-N
0.550
0.457
0.497
0.506
0.463
0.588
0.749
0.661
JHGDA
0.695
0.519
0.511
0.569
0.522
0.740
0.766
0.737
SpecReg
0.481
0.487
0.513
0,546
0.436
0.527
0.756
0.678
GIFI
0.636
0.521
0.493
0.535
0,501
0.623
0.719
0.705
PA
0.679
0.557
0.528
0.562
0.547
0.529
0.677
0.760
GAA
0.704
0.563
0.542
0.573
0.546
0.691
0.779
0.751
Table 2: Cross-network node classification on the Airport network.
Methods
D
C
C-
B1
B2
GCN
0.632
0.578
0.675
0.635
0.666
0.654
0.408
0.451
ANN-GCN
0.636
0.587
0.672
0.648
0.668
0.426
0.531
0.579
DANN
0.488
0.436
0,520
0,518
0,518
0.465
0.409
0.419
DANE
0.664
0.619
0.642
0.653
0.661
0.709
0.464
0.423
UDAGCN
0.684
0.623
0.728
0.663
0.712
0.645
0.471
0.468
ASN
0.729
0.723
0.752
0.678
0.752
0.732
0.524
EGI
0.647
0.557
0.676
0.598
0.662
0.516
GRADE-N
0.701
0.660
0.736
0.687
0.722
0.567
0.541
JHGDA
0.755
0.737
0,814
0.756
0.762
0.794
0.619
0.643
SpecReg
0.762
0.654
0.753
0.680
0.768
0.727
0.661
0.631
GIFI
0.751
0.737
0.793
0.755
0.739
0.751
0.653
0.642
PA
0.752
0.751
0.804
0.768
0.755
0.780
0.662
0.654
GAA
0.789
0.754
0.824
0.782
0.771
0.798
0.681
0.679
Table 3: Cross-network node classification on the Citation, Blog and Social network.
Methods
US - CN
US -+
DE
US -
JP
US -
RU
US - FR
CN + US
CN + DE
CN + JP
CN -
RU
CN + FR
0.042
0.193
0.160
ANN-GCN
0.092
0.189
0.269
0.186
0.213
0.210
0.133
0.201
0.105
0.102
DANN
0.302
DANE
0.272
0,250
0,280
0.210
0.186
0.279
0.108
0.228
0.170
0.184
UDAGGN
00M
00M
0OM
ASN
0.290
0,272
0.291
0.222
0.199
0.268
0.121
0.207
0.189
0.190
EGI
0OM
0OM
00M
OOM
OOM
0OM
0OM
0OM
OOM
OOM
GRADE-N
0.304
0.306
0.240
0.258
0.137
0,210
JHGDA
0OM
0OM
0OM
OOM
OOM
OOM
OOM
OOM
0OM
OOM
SpecReg
0.237
0,267
0.377
0.228
0.218
0.317
0.134
0.199
0.109
116
GAA
0.410
0.401
0.492
0.372
0,2881
0.453
0.302
0.400
0.351
0.293
Table 4: Cross-network node classification on MAG datasets.
5.2 BASELINES
We choose some representative methods to compare. GCN (Kipf Welling, 2016) further solves the efficiency problem by introducing first-order approximation of ChebNet. ANN-GCN (Wang et al., 2020) use the sparse k-nearest neighbor graph calculated from feature matrix as the input graph of GCN and name it ANN-GCN, DANN (Ganin et al., 2016) use 2-layer perceptron to provide features and gradient reverse layer (GRL) to learn node embeddings for domain classification DANE (Zhang et al., 2019) shared distributions embedded space different networks and further aligned them through adversarial learning regularization. UDAGCN (Wu et al., 2020) is a dual graph convolutional network component learning framework for unsupervised GDA, which captures
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knowledge from local and global levels adapt it by adversarial training. ASN (Zhang al., 2021) use the domain-specifie features in the network to extract the domain-invariam invariant shared features across networks. EGI (Zhu al., 2021) through Ego-Graph Information maximization to analyze structure-relevant transferability regarding the difference between source-target graph. GRADE- N (Wu al., 2023) propose a graph subtree discrepancy measure the graph distribution shift between source and target graphs. JHGDA (Shi et al., 2023) explore information from different levels of network hierarchy by hierarchical pooling model. SpecReg (You et al., 2022) achieve improving performance regularization inspired by cross- cross-po pross-pollinating between the optimal transport DA and graph filter theories. GIFI (Qiao al., 2024) uses a parameterized graph reduction module and variational information bottleneck to filter out irrelevant information. PA (Liu et al., 2024b) mitigates distribution shifts in graph data by recalibrating edge influences to handle structure shifts and adjusting classification losses to tackle label shifts.
5.3 EXPERIMENTAL SETUP
The experiments are implemented in the PyTorch platform using an Intel(R) Xeon(R) Silver 4210R CPU @ 2.40GH7, and GeForce RTX A5000 24G GPU. Technically, two layers GCN is built and we train our model by utilizing the Adam (Kingma & Ba, 2015) optimizer with learning rate ranging from 0.0001 to 0.0005. In order to prevent over-fitting, we set the dropout rate to 0.5. In addition, we set weight decay E {le 4, ... 5e 3} and & E {1, 10} for ANN graph. For fairess, use the same parameter settings for all the cross-domain node classification methods in our experiment, except for some special cases. For GCN, UDA-GCN, and JHGDA the GCNs of both the source and target networks contain two hidden layers (L = 2) with structure as 128 16. The dropout rate for each GCN layer is 0.3. We repeatedly train and test model for five times with the same partition of dataset and then report the average of ACC.
5.4 CROSS-NETWORK NODE CLASSIFICATION RESULTS
The results of experiments are summarized in Table 2 and 4, where the best performance is highlighted in boldface. Some results directly taken from (Shi et al., 2023; Pang et al., 2023). We have the following findings: It can be seen that our proposed method boosts the performance of SOTA methods across most evaluation metrics four group datasets with 16 tasks, which proves its effectiveness. Particularly, compared with other optimal performances in all datasets, GAA achieves a maximum average improvement of 1.80% for ACC. This illustrates that our proposed model can effectively utilize node attribute information. Our GAA achieves much better performances than SpecReg and JHGDA on all of the metrics dataset of Airport and most of the metries in a dataset of Citation. This can be explained by our method's 's use of attribute and topology structure. In most cases, GAA produces better performance than GRADE- (Wu et al., 2023) and JHGDA (Shi er al., 2023), which were published in 2023. This verifies the advantage of our approach. On most occasions, the feature graph produces a better result than the original graph. For example, in airport data, ANN-GCN performance averages better than 5.30% to GCN, and in citation datasets, performance averages better than 0.60% to GCN. Our findings affirm that the observed discrepancy in node attributes surpasses that of the topological misalignment, thus suggesting that the alignment of node attributes holds potential for yielding more substantial enhancements.
5.5 ABLATION STUDY
To validate the effectiveness of different components our model, we compare GAA with its three variants on Citation and Airport datasets.
GAA;; GAA without cross view similarity matrix Refinement to show the importance of comprehensive attribute and structure information.
GAAg; GAA without Ca to show the impact of attribute benefit alignment.
GAAg: GAA without C. and remove channel feature graph and only utilize LD show the effect of attribute(feature) graph impact.
According to Figure3, we can draw the following conclusions: (1) The results of GAA are consistently better than all variants, indicating the rationality of our model. (2) Both topology and feature

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Published as a conference paper at ICLR 2025
0.82
0,70
0.00
0.0
Accuracy
ACCUPICK
0.00
70.76
0.78
0,55
0.003
0.03
0.72
0.001
0.51
0.003
0.23
5.1
64 (pmemettee
123450 6
0.7050
7/78tb
kralio
0.20
0.70
0.800
AZCU
0.775
0.00
0.00
0.725
0.55
0.55
0.5
0.022
0.01
0.3
0.001
0.01
0.1
0.001
0.21
0.1
(1) parametero
parameer
(3) paremetory
Figure 4: The influence of parameters 8, r and & on Citation and Airport dataset.
information are crucial domain adaptation. (3) The cross-view similarity matrix can improve performance by enhancing and integrating feature information, benefiting the synergistic refinement of both attribute and topology.
5.6 PARAMETER ANALYSIS
In this section, we analyze the sensitivity of the parameters of our method on the Airport dataset and Citation dataset. As shown F in
Subfigure (4), the accuracy usually peaks at 2-3 with This is reasonable since increasing means more high-order proximity information is incorporated. On the other hand, extremely large k could also introduce noise that will deteriorate the performance. Furthermore, each dataset may require unique optimal value for k, determined by its inherent properties. For more information, please refer to the Appendix D. From Figure.5 Subfigure (1) (2) (3), we can see GAA has competitive performance on large range of values, which suggests the stability of our method.
6.5
(a) Citation
(b) Airport
Figure 3: The classification accuracy of GAA and
its variants on citation datasets and airport dataset.
CONCLUSION
In this paper, propose GAA framework to solve the GDA problem in cross-network node classification tasks. We reveal the importance both attributes and topology in GDA through both empirical and theoretical analysis, which minimizes attribute and topology distribution shifts based on intrinsic graph properties. Comprehensive experiments verify the superiority of our approach. In the future, we may strive to design new frameworks for other cross-network learning tasks, including link-level and graph-level. We will also deep into graph domain adaptation theory for developing more powerful models.
ACKNOWLEDGMENTS
This work is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants program. This work is also supported by the Vector Scholarship in Artificial Intelligence, provided through the Vector Institute.
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A
PROOF OF PROPOSITION
To facilitate the analysis, we adopt the following data assumption:
Definition The generated nodes consist of two disjoint sets, denoted as co and 0t. Each node feature x is sampled from N ((4,0,) for i E {0,1}.
(S)
Each set eN corresponds to the source graph and target graph compositions, respectively: 9 and
d The class distribution is balanced, such that P( cu) P(Y c1).
Theorem 2. For nodes E Vs and L E VT with aggregated featurrs f = GNN(2), the following inequality holds:
[P col P(y cu f.)il - O f. + f.. p1 (8)
f
p4 (T)
(16)
Proof. The conditional probability of class given the aggregated feature f can be expressed using Bayes' theorem:
Pff.y = co) = =o) Fly, = colf.)) IP y co)) = co) Pf IPyy, c1)
(17)
Under the assumption (y = co) = Fly = c1). we simplify this to:
y(f.yy =
P(y. co (o.f)) Pif,ly, = co) P(f,ly, ci)
(18)
Substituting in the expressions for the Gaussian distributions:
(f. (f.-1
exp
Ply. = -colfa)
(19)
(fu-Ho (8)
(Lo-
exp
+ exp
Thus, we have:
Ply calfu) Ply. co ff,)ll
P(f.ly, cco)
P(foly, co)
=11 =Il y co) IP(f.ly- =c, P(f.ly. = co) + P(f y =q)
(20)
[IP(flyy co) P(fel =a1 -P(ff y. = (f. y. = c1]l P(Llly -o) P(f.ly, =0 [
P(f.y c
Noting that the denominator is bounded, we substitute the probabilities of the Gaussian distributions into the expression:
Py = colfu) P(y. colff))l
(f.-14
(f.-
(2))2
(f.-u
(7)0 a²
(f., (f.-
(9)) p
(f--p) (f.
exp
G²
exp
exp
72
d²
exp(-A)
(21)
This leads
|P(y, colf..)P(y) = col/fa)l -
11
14 (2f,
11 -14
(5) (T)
) -(11 m (2f,
(T)
(8)
- (8) (8) (T)
-
TT)ll
(22)
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Published as a conference paper at ICLR 2025
This simplifies to:
[y = collu) - Ply. - colf.))l & O(I - -flll + +12,, (0()) -M1 1T).))
(23)
(a) We note that (.) = =111 1601 and A =
= (1)
i1; 11
Proposition 2 (Bound for D (P; For any " 2 O, and under the assumption that the prior distribution P over the classification function family H. defined, we establish a bound for the domain discrepancy measure D (P; X). Specifically, we have the following inequality:
(PPA)) 5O
Σ Σ JEVS JEVT
- -A4x 1 + iEVS JEVT
Σ
| -x
(24)
Proof. For notational simplicity. let h = (2 Go any i E Vs U Vr. Define ma(i)
Pr(y: = k 9X) for {0, 13, and let (h,, v.) = [h S y + m h, h,[k]]
We can express the difference in the loss functions as follows:
= Eyr
Σ
C1/2(19.8))
Ey
Σ
(25)
NT JEVT
Ns
iEVs
By N
Σ (2/2(1,, Us) - L (1e,
(26)
max NT)
NT
iE
JEVr
Using Definition 1, we derive:
27/2(h) (h) = max(Ns, NT)
Σ
Ey, L3 (h3. E, C7 (Mi,, wi)
NT
iEVs
JEV
1
(1))) Pr(m k) kh),
max NT)
NT
ieVs
JEVT
Σ
Σ
(),,
(h,, - (m)
Inax
N₂
iEVs
JEVT
Σ
Σ
(mlli) (cr/2(h), k) (h2, k)) + (mx - -m((1) C (hi,k))
max(NNs NT)
NT
iEV9
JEVT
(27)
Σ
Σ JEVT
Σ
()) + (lmx(i)) - m7x (1)
Nax NT)
Nr
ieVs
(28)
The last inequality holds since both nı (i) and L7 k)) are upper-bounded by 1, and we assume C2/2 k) < (h,,, k).
By applying Theorem 2, we obtain:
Σ [mm (i)) < O (IL.. f. 12 [f.
11 lif.
17
199 |f. 14 (T)
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Published as a conference paper at ICLR 2025
Thus, have:
f7 (N) 1 max NT)
Σ
Σ
nh (1)l2
NT
ieVs
jEVr k
0 Σ Σ
|[C fol12 + |[c,
Mi
(8) 1
|[,, 14 (T)
|Ic,
14 1
+
lIc.
140 1
LEVs JEV
0 Σ Σ I(A - (1xx))11
1
JEV
kEV's JEV
B DEFINITION OF AVERAGE FEATURE VALUE
We hope to quantitatively compare the differences in feature values between topology view and attribute view. Similarly, for the purpose of convenient comparison, we decided to calculate the average their feature values. Specifically, we first obtain a topology view matrix through topology filtering, multiplying A and X to F. Similarly, we perform attribute filtering by multiplying A and X to Fy to obtain a matrix of attribute view. So our topology average value is Fetuare, Ej= D /(d N) and attribute feature value is Features Σラ E [F5l/(d N).
C DESCRIPTION OF ALGORITHM GAA
Algorithm 1:
The proposed algorithm GAA
Input: Source node feature matrix X source original graph adjacency matrix A Target node feature matrix X Target original graph adjacency matrix A source node label matrix Y; maximum number of iterations 1)
Compute the feature graph topological structure Á and AT according X and XT by running KNN algorithm.
for 1 to do
75 GCN(AS XS)
5 GCN(ÁS X// embedding of source graph
Z GCN(AT. )"))
2f GCN(A", x/ embedding of target graph
75 and S through cross-view similarity matrix refinement to get SS.
zT and Zł through cross-view similarity matrix refinementto get ST
Maribute-Driver domain adaptive between 5 and ST // adaptive in two views Domain Adaptive Learning between 2 and z of constrained byy, and V constrained byğ?
Calculate the overall loss with (15(
Update all parameters of the framework according to the overall loss
end
Predict the labels of target graph nodes based on the trained framework. Output: Classification result Y
D PARAMETER ANALYSIS
a, [8, and chosen from the set f0.005, 0.01, 0.1, 0.5, 1,5). These values provide flexibility for adjusting the relative importance of different loss terms. (the number of neighbors for k-NN graph
construction) is typically & {1. 10} The optimal value for & depends the density and
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0.78
0.710
0.77
Accuracy
A000000
0.76
0.70
0.75
0.74
0.79
0.14
0.72
1985
3.001
0.01
0.1
0.5
0.031
0.01
6.3
0.5
1
910 k.raic
6.766
6014
01:00
0.07
0.67
0.875
0.660
Accuracy
0.67
0.85
0006
0.66
0.63
0.65
0.61
0.001
0.01
0.5
0.023
0.1
0.5
0.011
0.01
0.3
[ PSD
2 3
6 910
[1] parameter
(2) pansmeler
[5) persmiterr
K ratio
Figure 5: The influence of parameters a, 3, T and & on two social datasets.
connectivity of the graph. Due to extremely large it could also introduce noisy that will deteriorate the performance. Usually our largest k will be 5.
Airport Dataset: Often contains transportation networks with fewer nodes but complex edge rela- tionships. Given the sparsity of this dataset, a, / and should be set relatively higher to emphasize topology alignment and capture key structural relationships. a, B and 7 is selected from {0.1, 0.5). A smaller & could be more effective due to the sparser nature of these networks. We select le from (3.4).
Citation Dataset: This dataset often has higher node count and diverse structural characteristics. In such datasets, balance the impact of node attributes and topology. a, / and is selected from 0.5). A moderate value of capture relevant local structures could work well for this dataset. We select from 54.
Social Network Dataset (Blog and Twitch): Social networks often contain large number of nodes with rich attribute information but high variance in structural patterns. Emphasize attribute alignment since social networks tend to have highly distinctive attributes. Thus, attribute shifts are more sensitive to the values of a, 3 and т. which are selected from the set (0.01, 0.1, 0.5). Small k is recommended due to the dense connections
social networks. We select from {3.4), 4).
MAG Dataset: The MAG dataset is large and diverse, containing losts of classes with various relationships and rich metadata. Structural and attribute alignment are key factors. In this context, attribute shifts are both important to the values of a,3 and which are selected from the set 10.1, 0.5). The parameter works well in this context, enabling the model capture higel local and global structural information within the graph. We select & from {4,5).
E T-SNE SAMPLE
(a) UDAGCN
(b) JHGDA
(c) GRADE
(d) SpecReg
(c) GAA
Figure 6: Visualization of learnt representations of different methods on D-A task of dataset.
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Types
Datasets
k
U-B
0.5
0.5
0.01
U-E
0.1
0.1
0.01
B-+U
0.01
Airport
B-E
0.1
0.1
E
0.5
0.5
0.1
R-+B
0.5
0.5
0.1
A-D
0.1
0.1
0.1
D
0.1
0.01
A-C
0.5
0.01
Citation
C-+A
0.1
0.1
C->D
0.1
0.1
D-C
0.1
0.1
0.1
BI-+B2
0.1
0.1
0.1
Blog
B2-+B1
0.1
0.1
DE >EN
0,1
0.1
0.01
2
Twitch
EN-DE
0.1
0.5
0.5
US-+CN
0.1
0.1
US-DE
0.1
0.1
US JJP
0.5
0.01
MAG
US-RU
0.1
0.1
0.5
US-*FR
0.1
0.1
0.1
CN-US
0.1
0.1
0.01
CN->DE
0.1
0.1
0.5
CN-+P
0.1
0.1
0.01
CN-RU
0.1
0.1
>FR
0.01
0.1
Table Experiment hyperparameter setting Value.
F TIGHTNESS OF BOUNDS
To evaluate the tightness of our bounds, we conduct additional experiments to verify the effects of node attribute divergence and topology divergence independently. The following experimental detail settings are designed to verify these divergences.
F.1 ATTRIBUTE DIVERGENCE
To evaluate the impact of graph attribute discrepancy on GDA, we designed an experiment for this purpose. In this experiment, we provide node classification tasks across different graphs under different attribute discrepancy with same topology structure. In this procedure, we aim to generate a collection of graph datasets, where each graph is characterized by fixed adjacency matrix A, consisting of 100 nodes with average degree of 0.3, and node attribute matrices X randomly simulated from Gaussian-distributed samples. The specific steps are as follows: Each graph G, = (A, X2) shares the same fixed adjacency matrix A, representing the same graph topology. A is predetermined and defines the connectivity between nodes, remaining consistent across all generated graphs. Node attributes X; for each graph G; are generated using 'make-blobs' function from scikit-learn. Parameters for 'make-blobs': Number of nodes: Yisamples 100, representing the total number of nodes in the graph. Number of clusters: centers 2, corresponding two distinct classes. Veaatures = 10, meaning each node is described by a 10-dimensional feature vector. cluster_sti is variable parameter uniformly sampled from the range [0. 2). determining the dispersion of node features within each cluster. We construct a dataset of 1000 graphs, (G; = X,)) 1,000, where: A: the adjacency matrix, remains fixed across all graphs, representing the struetural relationships between nodes. X,: the feature matrix, varies between graphs. The variance of the node features determined by cluster_std which is uniformly sampled for each graph to introduce diversity in the node attributes. GAA is trained for 100 epochs different attribute variances. After training, we reports three key metrics for each dataset: the bound value, LA (loss value), and the target graph accuracy. To ensure that the bound value and loss value are on the same scale, we normalize the bound value by dividing it by the number of nodes, i... 100. As illustrated in Figure 6(a), both the bound value and the loss value of the model increase as
fixed source graph and target graphs with
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the attribute discrepancy grows. Conversely, the classification performance declines with increasing attribute discrepancy, highlighting that the bound attribute component is closely related to the GDA performance.
F.2 TOPOLOGY DIVERGENCE
This procedure involves generating 1000 graphs by Stochastic Block Model (SBM), probabilistic model for community-stree structuredd graphs. Each graph consists of different adjacency matrix with uniformly distributed edge weights and a node attribute matrix X with fixed-dimensional feature vectors. The generation process detailed below: The graph G. = (A,, Xa) for each instance is generated using the SBM. SBM parameters
as follows: community contain 100 nodes( num modenoddes- 100); sizes = nom podes num _nddes pum nodes where the graph is divided into
2 muunnnummeeeee
two communities of approximately equal size, which can be seen as 2 classes. Inter- and Intra-
where = 0.8 denotes the probability community connection probabilities: probs = 2 p
of edges forming within a community and 2 different communities. To incorporate variability in edge strengths, the weights of edges in the adjacency matrix A, are drawn from a uniform distribution. Nonexistent edges are assigned a weight of 0, thereby preserving the sparsity structure dictated by the Stochastic Block Model (SBM). Each adjacency matrix A,, representing graph G; is a symmetric n. X n matrix. Additionally, each graph G is associated with node attribute matrix X;, where X; contains rows, corresponding the attributes of the n nodes. Each nodes is a fixed 10-dimensional vector, ensuring consistent node attribute dimensionality across all graphs. All elements of X; are a constant value of ensuring uniform node attributes across the dataset. The dataset comprises 1000 graphs, each containing 100 nodes, represented as {G; = (A,, X.) 10000 In this representation: A, varies between graphs, following the Stochastic Block Model (SBM) with uniform edge weights, while X; is fixed matrix where each of its 10-dimensional elements is set to GAA is trained for 100 epochs on a fixed source graph and target graphs with different topology variances. After training, we reports three key metries for each dataset: the bound value, LA (loss value), and the target graph accuracy. Similarly, normalize the bound value by dividing by the number of nodes, i.c., 100. As illustrated in Figure 6(b), both the bound value and the loss value of the model increase as the attribute discrepancy grows. Conversely, the classification performance declines with increasing topology discrepancy, emphasizing that the bound's topology component is also closely linked to the GDA performance.
TO 0.08 denotes the probability of edges forming
G MODEL EFFICIENT EXPERIMENT
To further investigate the efficiency of GAAo, Table?? reports the running time comparison across various algorithms. We also compared the training time and GPU memory usage of common baselines UDAGCN and recent SOTA method, JHGDA, which aligns graph domain discrepancy hierarchical levels. As shown in Table, the evaluation results on airport dataset further demonstrate that our method achieves superior performance with tolerable computational and storage overhead.
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Published as a conference paper at ICLR 2025
2.00
100
Bound Value
Target Accuracy
Loss Value
1.50
Accuracy (%6
1.25
Value
1.00
90
0.75
85
0.50
0.25
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
attribute value diveraence
(a) Attribute component
2.00
100
Bound Value
Target Accuracy
1.75
Loss Value
1.50
Accuracy (%6
1.25
Value
85
0.75
80
0.25
0.0
0.5
1.0
1.5
2.0
2.5
3.0
topoloav value divergence
(b) Topology component
Figure Visualization of bound value and LA value.
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Published as a conference paper at ICLR 2025
Dataset
Method
Training Time (Normalized UDAGCN)
Memory Usage (Normalized UDAGCN)
Accuracy(%)
UDAGCNB JHGDA
0.607
1.314
1.414
0.695
U-BB
GAA-
0.504
0.514
0.697
GAA
1.063
1.113
0.704
UDAGCNB
JHGDA
1.423
1,513
0.519
PA
0.509
0.557
U-B
GAA.
0.507
0.513
0.556
GAA
1.109
1.098
0.563
UDAGCNB
0.510
JHODA
1.501
0.569
PA
0.502
0.497
0.562
B-F
GAA,
0.507
0.503
0.566
GAA
1.048
1.107
0.573
Table 6: Comparison of Training Time, Memory Usage, and Accuracy on Airport datset.
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Reviews:
[{"review_id": "77a4QWYyxi", "reviewer": "Reviewer_cb1H", "summary": "This paper addresses the Graph Domain Adaptation (GDA) problem by investigating the critical role of node attributes in domain shift. This study reveals that node attributes contribute significantly to domain discrepancy. Through theoretical analysis using the PAC-Bayes framework, the authors prove that the domain shift is bounded by both topological structure differences and node attribute differences. Based on this insight, they propose Graph Attribute-driven Alignment (GAA), a novel method that aligns both structural and attribute information between domains. Experiments are conducted on two datasets: the Airpot network, Citation, Blog, and Social network.", "questions": "- Please provide more information about the motivation behind the attention-like strategy (Eq. 8).\n- How does the contribution of $||att_f^S - att_f^T||_2^2$ compare to $||att^S - att^T||_2^2$?\n- Which is more important for the performance? $L_D$ or $L_A$?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- Unlike previous methods that primarily focused on graph structural information, this approach provides a novel perspective by emphasizing the importance of node attributes in GDA.\n- A solid theoretical analysis proved that domain discrepancy comes from both graph structure and node attributes.\n- Strong experimental results.", "weaknesses": "- The motivation for using an attention-like strategy is unclear.\n- L_A contains both $||att^S - att^T||_2^2$ and $||att_f^S - att_f^T||_2^2$. There is no clear demonstration of which component is more important for performance. To highlight the effectiveness of aligning attributes, we need to check the effectiveness of $||att_f^S - att_f^T||_2^2$ (with/without Coss-view Matrix Refinement).\n- The effect of $L_D$ is unclear. It is necessary to verify the contribution of $L_D$ compared to $L_A$, as $L_D$ is also a domain alignment strategy.", "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": "mYlelWw5L9", "reviewer": "Reviewer_v8TX", "summary": "This paper aims to address the Graph Domain Adaptation problem by incorporating both structural information and node attribute values, the latter of which is often neglected by previous approaches. The paper first presents the significance of considering node attribute shifts in GDA. It then derives a PAC-Bayesian analysis, which provides an upper bound on the overall discrepancy in expected loss. This bound relies solely on the divergence between the topologies and node attributes of the source and target graphs. The proposed method combines the topological and node feature representation through an attention module and performs domain adaptive learning between the source and target representations. Experiments showed good performance of the proposed algorithm, compared with multiple baselines.", "questions": "Please refer to the weakness section.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. This paper makes a good observation that node attributes are important for domain adaptation tasks and then follows up with a theoretical analysis.\n2. The authors conducted comprehensive experiments to validate the algorithm's performance with a sufficient number of baseline methods compared.\n3. The ablation study shows the effectiveness of the proposed method and its key components.", "weaknesses": "1. **Writing**: The writing of the paper can be improved. In particular, citation errors appear on the very first page of the paper. The author should proofread the paper more carefully before submission. Some sentences contain grammatical errors.\n2. **Theoretical novelty**: The theoretical analysis of the paper is a direct application of the PAC-Bayesian results derived from [1], including the problem formulation and Theorem 1. The discrepancy between subgroups in [1] can be naturally applied to graphs in the source/target domain. \n3. **Tightness of bounds**: The upper bound derived in Proposition 1 is novel, yet I am curious if the upper bound is tight enough to be useful. It would be great if the author could provide some numerical evidence showing the tightness of the bound or experiments that show how the improvement of performance relates to the divergence of both topological structure and data features over training. \n4. **Scalability**: The scalability of the proposed algorithm is a concern, especially when applied to large graphs. The GAA algorithm requires constructing and processing both topology and attribute views, with a cross-view similarity matrix that grows with the size of the graph. This can lead to high computational costs, particularly when both views are combined through an attention module. Running time analysis and comparison with existing baselines are needed.\n\n**References**\n[1] Ma, J et al. Subgroup generalization and fairness of graph neural networks.", "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": "UmoUSAUU1K", "reviewer": "Reviewer_JjmB", "summary": "This paper studies the problem of Graph Domain Adaptation and shows the significance of the graph node attribute for graph domain alignment. Then, they propose a novel cross-channel module to fuse and align both views between the source and target graphs for GDA. Experimental results on a variety of benchmarks verify the effectiveness of the method.", "questions": "See above.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The studied problem is interesting and important.\n- The paper is well-organized and clearly written.\n- The experiments are extensive and helpful to validate the effectiveness of the model.", "weaknesses": "- Some citation styles are not correct, e.g., Line 37.\n- The paper lacks some of the latest baselines such as \"Information filtering and interpolating for semi-supervised graph domain adaptation (2024)\" \n- Masking node attributes could have the risk of losing crucial information. How about alignment in the embedding space?\n- I suggest that the authors should use some tSNE to show the effectiveness of the method.\n- The parameter sensitivity needs more analysis. How to apply your method to a new dataset.", "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": "yCqz4REbkB", "reviewer": "Reviewer_KMc6", "summary": "In this paper, we focus on the graph domain adaptation (GDA) problem and propose a novel cross-channel graph attribute-driven Alignment (GAA) algorithm, which aims to address the distribution differences between the source and target graphs, especially the differences in node attributes and topology.", "questions": "1. This paper does not analyze the time complexity of the model, whether it is universal for larger scale node classification problems, and whether it can ensure the operation efficiency of the model on large-scale problems. For example, in the construction of attributed graph using KNN, is it too time-consuming to successively calculate the similarity score between nodes to connect edges?\n2. The article is generally on the supervised domain adaptation problem. Personally, I think this application scenario is relatively limited in the general scenario.\n3. The article does not give detailed hyperparameter Settings, for example, in the weight design of the loss function, although the experiment carries out the analysis of different weights, the final setting value is not given. In addition, for the weight setting of the loss function, will the weight of a loss function be too low to cause it to not work in the model training process?\n4. Can it be compared with the latest GDA method?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Through theoretical and experimental analysis, the difference of eigenvalues distribution between graph topology and node attributes is analyzed, which reveals that the difference of node attributes is more significant than the difference of topology structure, and further emphasizes the importance of node attribute alignment.\n2. The Cross-Channel Module is proposed to align the graph topology and node attributes simultaneously. This module effectively reduces the domain differences by fusing and aligning the two views of the source and target images.", "weaknesses": "1. The whole setting of this paper is to perform domain adaptation (GDA) under supervised conditions, that is, the annotation of the target domain is involved in the training process. However, for most practical problems, the target domain is extremely lack of data annotation or even no annotation, which lacks certain considerations for practical applications. If the target domain is annotated why not design the model for the target domain instead of introducing the source domain.\n2. In the experiments section, we see that the latest method you currently cite is the result of 2023, and the comparison test of the relevant latest method in 2024 is missing. And on the types of downstream tasks, there is a lack of evaluation of other tasks, such as graph classification tasks.\n3. This paper lacks the time complexity analysis of the model, and does not analyze the overall operating efficiency of the model. In particular, GAA algorithm introduces additional calculation steps (such as cross-view similarity matrix), which may lead to increased computational complexity. On large-scale graph data, the efficiency and scalability of the algorithm may become an issue.\n4. In the model graph part of this paper, in the target domain part, the legend labels of the attribute graph and the topology graph are wrong. The text annotation at the bottom of Figure 1 is ambiguous from the data in the bar graph (the difference in the distribution of eigenvalues is greater for topological graphs than for attributed graphs?).", "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)