SAGA: Structural Aggregation Guided Alignment with Dynamic View and Neighborhood Order Selection for Multiview Graph Domain Adaptation
Authors: Ruiyi Fang
Jingyu Zhao
Shuo Wang
Ruizhi Pu
Bingheng Li
Jiale Cai
Zhihao Li
Zihao Jing
Jian Zhu
Song Tang
Charles Ling
Boyu Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Graph Domain Adaptation
Primary area: transfer learning, meta learning, and lifelong learning

Abstract: Graph domain adaptation (GDA) transfers knowledge from a labeled source graph to an unlabeled target graph to alleviate label scarcity. In multi-view graphs, the challenge of mitigating domain shift is constrained by structural information across various views. Moreover, within each view, structures at different hops capture distinct neighborhood levels, which can lead to varying structural discrepancies. However, existing methods typically assume only a single-view graph structure, which cannot effectively capture the rich structural information in multi-relational graphs and hampers adaptation performances. In this paper, we tackle the challenging Multi-view Graph Domain Adaptation (MGDA) problem by proposing Structural Aggregation Guided Alignment (SAGA) that aligns multi-view graph data via dynamic view and neighborhood order selection. Specifically, we propose the notion of Structural Aggregation Distance (SAD) as a dynamic discrepancy metric that jointly considers view and neighborhood order, allowing the dominant view–order pair to vary during training. Through empirical analysis, we justify the validity of SAD and show that domain discrepancy in MGDA is largely governed by the dominant view–order pair, which evolves throughout training. Motivated by this observation, we design SAGA, which leverages SAD to dynamically identify the principal view-order pair that guides alignment, thereby effectively characterizing and mitigating both view- and hop-level structural discrepancies between multi-view graphs. Experimental results on various multi-relational graph benchmarks verify the effectiveness of our method.

Published as a conference paper at ICLR 2026
SAGA: STRUCTURAL AGGREGATION GUIDED ALIGN- MENT WITH DYNAMIC VIEW AND NEIGHBORHOOD ORDER SELECTION FOR MULTIVIEW GRAPH DOMAIN ADAPTATION
Ruiyi Fang'
Jingyu Zhao'
Shuo Wang² Ruizhi Pul
Bingheng Li Jiale Cai
Zhihao Li
Zihao Jing' Zhu Jian"
Song Tang
Charles Ling'
Boyu Wang 1.6+
Western University
University Electronic Science and Technology of China *Guangdong University of Technology
3 Michigan State University
5 University of Shanghai for Science and Technology
Vector Institute
(r fang322, jzhao786, rpu2, jcai336, 2 i 1344,, charles i ng @uwo ca
runner21st 1anee2lsss@@mmml1 com libinghe@msu. edu bwangåcsd. uwo ca
ABSTRACT
Graph domain adaptation (GDA) transfers knowledge from a labeled source graph to an unlabeled target graph to alleviate label scarcity. In multi-view graphs, the challenge of mitigating domain shift constrained by structural information across various views. Moreover, within each view, structures at different hops capture distinct neighborhood levels, which can lead to varying structural discrepancies. However, existing methods typically assume only a single-view graph structure, which cannot effectively capture the rich structural information in multi-relational graphs and hampers adaptation performances. In this paper, tackle the chal- lenging Multi-view Graph Domain Adaptation (MGDA) problem by proposing Structural Aggregation Guided Alignment (SAGA) that aligns multi-view graph data via dynamic view and neighborhood order selection. Specifically, we propose the notion of Structural Aggregation Distance (SAD) dynamic discrepancy metric that jointly considers view and neighborhood order, allowing the dominant view-order pair to vary during training. Through empirical analysis, we justify the validity of SAD and show that domain discrepancy in MGDA is largely governed by the dominant view-order pair, which evolves throughout training. Motivated by this observation, we design SAGA, which leverages SAD to dynamically identify the principal view-order pair that guides alignment, thereby effectively character- izing and mitigating both view- and hop-level structural discrepancies between nieti-view graphs. Experimental results on various multi-relational graph bench- marks verify the effectiveness of our method. The source code is available at https: //pps:///itte hub. com/ www.unggy////ooo SAGA.
INTRODUCTION
Multi-view graphs, which involve a set of nodes with multiple relations, are prevalent in the real world because of their extraordinary ability in characterizing complex systems (Qu al., 2017). Some typical instances include citation networks, social networks, and knowledge graphs (Wu et al., 2023b; Pan & Kang, 2023), which have multi-relational structures (Xia et al., 2025; Yu al., 2024; 2025c; Yang et al., 2023b). Recently, this challenge has been particularly acute in multi-view graphs, where annotating structured data is especially difficult and costly, leading pervasive label scarcity (Xu et al., 2022b; Huang et al.. 2025a; Zhuo et al., 2025). To address this challenge, Multi-view Graph Domain Adaptation (MGDA) has emerged as an effective paradigm to transfer knowledge from labeled multi-view source graphs to unlabeled multi-view target graphs (Chen et al., 2019; Shi al., 2024).
"Corresponding author

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Published as a conference paper at ICLR 2026
Exinling, GIDA: single
As shown in Fig. 2, unlike other single-view graphs, the differences in multi-view graphs stem from their diverse topological structures. Therefore, during MGDA, it is essential to consider both domain shift and cross-view shift. Harly works overlook capturing multiple relation structures, widely existing aspect of graph data. Furthermore, existing techniques fail to capture structural dependencies across multiple graph hops (Yang al., 2024a; 2023c; 2024b; Huang et al., 2023; 2024a), thereby limiting their ability to estimate structural distributional discrepancies. Thus, natural question arises: (Ω) How can we quan- tify structural disparities across different hops between multi-view source and target graphs? To address Q. we introduce the Structural Aggregation Distance (SAD). simple yet effective metric for evaluating structural disparities across various hops. Unlike prior approaches that measure discrepancies only at the first hop level (Fang et al., 2025b), SAD explicitly incorporates both the aggregation process and the distribution of node attributes to capture hop-specific differences. Empirical studies conducted on real-world datasets justify the efficacy of this novel metric in evaluating the quality of views multi-view graphs. As shown in Fig. 3, smaller Structural Aggregation Distance (SAD) generally indicates small cross-net cross-network perfor- mance gap between domains. Moreover, since the optimal performance may occur at different hops, each associated with the minimum SAD discrepancy, an effective approach requires dynamically aligning structural information across both view and hops during training.
(U) Maticcier GIAA (MGDAY resti
Danan
Tagget Greah
Figure I: Illustration of the graph domain adapta- tion (GDA) task and MGDA (Mudi-viee GDA).
To address the aforementioned challenge, we propose Structural Aggregation Guided Alignment (SAGA), which leverages the dominant view identified as the one achieving the minimum SAD across hops to guide the alignment process. Specifically, SAGA dynamically explores unsupervised view alignment during training. adaptively adjusting views and hop-wise aggregation so as to align structural information across different views. This dynamic hop alignment ensures that representations are not constrained to a fixed hop level or view, but instead are adaptively synchronized in order to capture the view and hop-specific structural disparities. Afterward, by leveraging the embedding with corresponding source and target dominant view-hops structures to guide both cross-view and intra-view alignment, we aim mitigate domain shift as well as view shift.
We underscore the primary contributions of this study as follows:
We first investigate Multi- Multi-View Graph Domain Adaptation (MGDA). To this end, we propose the Structural Aggregation Distance (SAD). novel metric that quantifies structural disparities across different hops, thereby enabling a comprehensive evaluation of structural shift.
Our empirical observations justify the design of SAD, upon which we introduce SAGA. In this framework, the view with the minimum SAD is identified through dynamic view and hop selection, enabling dominant-view alignment to mitigate both cross-view and intra-view discrepancies.
We validate our approach through extensive experiments on extensive real-world datasets. The proposed SAGA framework consistently achieves state-of-the-ar performance, demon- strating its superiority in addressing the challenges of multi-view graph domain adaptation.
2 RELATED WORK
2.1 MULTI-VIEW GRAPH LEARNING
Unsupervised domain adaptation 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: Shen et al., 2023; Yang et al., 2023a; 2022: 2025b; Xu et al., 2023a; 2022a; Yang et al., 2025a), 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 al., 2023; Jing al., 2021;
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Zeng et al., 2025; Xu et al., 2023b; Zhang & He, 2025b). Some novel settings in domain adaptation have also gotten a lot of attention, like source-free domain adaptation (SFDA) (Yang et al., 2021; Xu et al., 2025b;a). test-time domain adaptation (TTDA), and relevant robustness (Wang et al., 2022; Guo et al., 2026; Yang et al., 2025c; Min et al., 2025; Huang et al., 2024b; Xu et al., 2023a; Li et al., 2025b; Ji et al., 2025; 2024). 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; Shen et al., 2025). AVDNE (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 retss-network embedding from source and target data to minimize the maximum mean discrepancy (MMD) directly. GraphAE (?) analyzes node degree distribution shift in domain discrepancy and solves by aligning pessage--psssngg routers. DM-GNN (Shen et al., 2023; Huang et al., 2025b; Fang et al., 2022) proposes method to propagate node label information by combining its own and neighbors' edge structure. Furthermore, some recent research work focuses on multi-view learning in federated learning and remote sensing (Zheng et al., 2025c;b;a).
2.2 GRAPH DOMAIN ADAPTATION
UDAGCN (Wu al., 2020; Yang et al., 2026) develops a dual graph convolutional network by jointly capturing knowledge from local and global levels to adapt it by adversarial training. ASN (Zhang al., 2021) separates domain-specific and domain-invariant variables by designing private en-coder and uses the domain-specific features in the network to extract the 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 eliminate structural shifts. SpecReg (You et al., 2022) used the optimal transport- the GNNs' Lipschitz constant be achieved by spectral smoothness and maximum frequency response. 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. Identifying the differences between the target and source graphs in GDA is crucial. For graph-structured data, several studies have explored cross-grap graph knowledge transfer using graph domain adaptation (GDA) methods (Shen & Chung, 2019; Dai et al., 2022: Shi et al., 2024). Some graph information alignment- hignument-basedd methods (Shen et al., 2020a:b; Yan & Wang, 2020; Shen et al., 2023; Fang et al., 2026; Xie et al., 2025a) adapt graph source node label information by integrating global and local structures from both nodes and their neighbors. UDAGCN (Wu et al., 2020) introduces dual graph convolutional network that captures both local and global knowledge. adapting it through adversarial training. Furthermore, ASN and GraphAE (Zhang et al., 2021: Guo et al., 2022) consider extracting and aligning graph-specific information like node degree and edge shift, enabling the extraction of shared features across networks. SOGA (Mao et al., 2024) is the first incorporate discriminability by promoting structural consistency between target nodes of the same class, specifically for source-free domain adaptation (SFDA) on graphs. SpecReg (You al., 2022) applies an optimal transport- based GDA bound and demonstrates that revising the Lipschitz constant of GNNs can enhance performance through spectral smoothness and maximum frequency response. JHGDA (Shi et al., 2023) tackles hierarchical graph structure shifts by aggregating domain discrepancies across all hierarchy levels to provide comprehensive discrepancy measure. ALEX Yuan al. (2023) creates a lahel-shift- enhanced augmented graph view using low-rank adjacency matrix obtained through singular value decomposition, guided by a contrasting loss function. SGDA (Qiao et al., 2023) incorporates trainable perturbations (adaptive shift parameters) into embeddings via adversarial learning, enhancing source graphs and minimizing marginal shifts. PA (Liu et al., 2024) mitigates structural and label shifts by recalibrating edge weights to adjust the influence among neighboring nodes, addressing conditional structure shifts effectively. And other graph foundation models (Wang et al., 2025b;a; Shen et al., 2024b; Xie et al., 2025b; Li et al., 2025a) focus on solving multi-domain problems.
shared features
GDA bound for graph data and discovered that revising
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04
144 341
Lock
(a)
A2
(b) CN US
(c) CN
(d) DE FR
Figure 3: We report SAD performance in ACM and MAG datasets. Training dynamics of SAGA under different settings. The r-axis denotes training epochs, the left y-axis represents the classification loss
discrepancy between source and target domains (L, - L.)', and the right y-axis shows the Structural Aggregation Distance (SAD). Only the dynamic SAD consistently decreases while simultaneously driving down the source-tane target loss gap. indicating effective alignment. In contrast, fixing hops or views yields suboptimal results, where SAD may decrease but the loss discrepancy does maintain sustained reduction.
EMPIRICAL STUDY
Definition 1 (Mutliview Graph Domain Adaptation). We consider an undirected multi-relational where denotes the node set with N nodes, and & repre- sents the edge set in the v-th view. X TRNNxd is the feature matrix, and Y is the label matrix with C' classes. For each view v, A is the adjacency matrix, D" the degree matrix, and the normalized
graph G (A', [LA",VE,: V, Ev,X.
adjacency with self-loops is A" = (D" + 1 ( + 1)-2, 1 We denote the source and target graphs as G {AS.1
A5. X55 Y and G {AT.1 tively. Labels (i.e.. Ys and Yr) express the same meanings. Yr is fully unknown, which indicates an unsupervised problem in the target mutli-view graph.For convenience, we define er E Vr, us E Vs, ky E Kr and kg Ks.
AT .T). respec-
Dynamic View By
Structural Aggrega- tion Distance (SAD). To quantify view dis- crepancies across dif- ferent hops in domain shifts, define the Structural Aggregation Distance(SAD), Fol- lowing the aggregation rule of SGC Wu al. (2019), the K K-hop ag- gregated features of the v-th view puted as:
Encoder I' kop
Dynumic
App
Encoder
Decoder
5 view
SK b4
Recalculate $AD
Multi-relational Graph
Sirnctural Agonegation Dillans
v'KK"
1 siene
Ageropatio Algusso
Encoder
Decoder
Dynammic Auges
kop
N kpp
Encuder
Vyy
Decoder
Dinomic view
X.S.Ks = (ASivsi xs.
X
TTNaa (TTTTYK'" by
(1)
Figure 2: Overview of the SAGA framework. encodes multi-view graphs, dynamically selects dominant view-hop pair via SAD, aligns source-target embeddings under unified optimization.
Let Yr. and X...k denote the aggregated centroids of source
and target nodes' features after structural aggregation. Then, the K hop SAD is defined as
SAD =1 N
Σ
Σ 1
N
Z,EX K.K
ZJEXT
Intuitively, SAD measures the structural discrepancy between source and target distributions in view Vr and us at hop Kr E (1..... 5).Ks E (1,... .5). However, given the above definition. SAD cannot be directly applied to assess view quality during training. To address this limitation, we
Details Difinition of F.r in Section G of Appendix.
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propose variant method that quantifies structural discrepancy in the embedding space throughout the training process.
SAD t
12 (Z 2 ( 1|2
where - T and 2 E Z are X E X Kr and X during training procedures and {us, vT J. k = {kg, kr). In definition.
X Skks embedding
SAD = min SAD 8
v.K
The experimental setup follows the main experiment, with SAD adjusted into two variants: SAD K PSP--PAP" which fixes PAP PSP in ACM dataset and PAP - PP MAG dataset, and SAD,, which fixes K with dynamic views. In Fig. setting, we fix PSP PAP and K as 2
Based on the Fig. 3, we observe that dynamically adjusting SAD yields the best performance, as it most effectively reduces the discrepancy between the source and target domains. In contrast, fixing either the hops or the views for alignment results in suboptimal outcomes. Even when the discrepancies within fixed hops views continue to decrease, the overall shift between the source and target domains does not consistently decrease.
METHOLOGY
Building the empirical evidence in Section 3, present Structural Aggregation Guided Alignment (SAGA) framework. Our methodology follows a progressive design: (i) obtain scalable graph embedding for downstream operation, (ii) leverage SAD to evaluate structural disparities across views and hops to identify the dominant view as the guide for adaptation,
4.1 SCALABLE GRAPH ENCODING
Unlike most GNN-based approaches (Mo et al., 2023a;b), we decouple graph propagation and dimensionality reduction to improve scalability. Initially, we perform propagation on the node features separately for each view, acquiring view-specific aggregated features. Similar to the approximate personalized propagation in Chien et al. (2020), we introduce the features of the original node as teleport vector in each layer the propagation process:
X =X
X = (1 - a"" aX
(5)
where X acts both the starting matrix and the teleport set for each view. The hyper-parameter [0, 1] represents the teleport probability. E [0, K 1] and the aggregated features X X These features are then fed into shared encoder for dimensionality reduction:
z"= fe("))
where Z"
Nxd, denotes node representations in the v-th view. This setup avoids the time- consuming graph convolution operations during training. Subsequently, the representations for each view are fed into shared decoder for the reconstruction of view-specific aggregated features. Efffective training of each view is ensured by optimizing the reconstruction MSE error:
ge (Z") X
V
N
CR NV
|X -
(8)
where the encoder fe(.) and the decoder ge(-) are both implemented using a Multilayer Perceptron (MLP) in this study. After applying the scalable graph encoding to extract graph information, the
output node embedding for the source and target graph is denoted as Z T.Kr = (2 ..T....TTTTT. TT
S
Z5.kk }.
Details analysis on the K presented in Section F of Appendix.
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4.2 DYNAMIC NEIGHBORHOOD ORDER SELECTION VIA SAD
For each view and hop K E {1, ... Kmax), we measure the structural discrepancy as SAD, In practice, we usually set Kmax The dominant pair (v's: K3s,, (vy, K7) is defined
v" v",k"=
arg min SAD v.K
(9)
where o- = (vg, 24), K- = (kyr kG) provides the most transferable structural signal during training.
SAD guided weight. Rather than placing weights directly on the hop index K, we assign weights to the corresponding discrepancies SAD such that smaller values receive higher emphasis. Formally, we define
exp( Exp((XSSMD ASAD
Wo,k
K 2j=1 E exp(- (xp(-)))
A 0,
(10)
where Wo, {wes kis Wey for aand the temperature parameter À controls the sharpness of the weighting. This construction ensures that the hop with the minimal SAD,, naturally obtains the largest weight, while still allowing other hops to contribute proportionally to their discrepancies.
Intra-alignment to the dominant view. For cross-view alignment, we employ the discrete optimal hop Kn arg mink SAD for each view and the global dominant pair (v* kk"),, and minimize
LIa NEV, E.E Se
NEV. E. Woguks
2 (25.) (11)
where TT and - denote the embedding matrices for view By in the target domain and view Uy in the source domain, respectively (computed after le aggregation hops); to reduce computational cost, we set = 1 by default in training stage. Only calculate other hops in obtain SAD. During LIA. it be constrained through aligning the each dominant view space. To this end, it can obtain uniform dominate guided embedding.
Cross-alignment to the dominant view.
NT Ns cxp
(Z ZT. = log Ns 2u
cos
/T)
(12)
J=1
203 where Z,3 2.5. and ZT. is the non-linear projection of S.kk and ZT sim(() refers the cosine of similarity and r is the temperature parameter. To integrate the dominant view and dynamically aligned hops, adopt contrastive learning objective. Representations from each view are projected into a shared latent space, and aligned with the dominant pair view:
LCA (Z
8,k
ZT
T.k oj
+
E(Z
T.K. S.A
(13)
where ((:-)) is the alignment loss based on cosine similarity. By applying these loss, we align both intra-domain and cross-domain embeddings.
4.3 TARGET NODE CLASSIFICATION
The source classifier loss Ls (fs (ZS). S) is to minimize the cross-entropy for the labeled data node in the source domain:
1 Ns
Ls (fs (28),
y
Σ vr s log (i,)
(14)
Ns
where v denotes the label of the i-th node in the source domain and oe are the classification prediction for the i-th source graph labeled node vs E vs, To utilize the data in the target domain, we use entropy loss for the target classifier fr:
Nr
Cr (fr (Z)) = Nr
Σ of log (i
(15)

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Methods
Merric
CN JUS
US >CN
JP >CN
CN >JP
DE IFR
FR >DE
RU >US
US +RU
DE
JCN
CN
IDE
GCN
ACC
0.351
0.263
0.327
0.335
0.319
0.302
0.301
0.317
0.281
0:291
macro-F]
0.217
0.218
0.221
0.201
0.203
0.167
0,233
0.320
0.167
0.223
ACC
0.325
0.316
0,361
0,309
0.324
0.253
0.265
0.339
0.281
0.316
macro-Fi
0.320
0.249
0.221
0.323
AdaOON
ACC
0,188
0.483
0.389
0.425
0.431
0.314
0.570
0.435
0.359
0.363
0.362
0.285
0.369
UDAGCN
ACC
0,455
0.53
0.451
0.414
0.382
0.364
0.389
0.421
0.356
0.349
0.275
0.262
0.361
0.253
GRADE-N
ACC
0.421
0.467
0.376
0.404
0.455
0.375
0.353
0.429
0.358
0.362
macro-F1
0.241
0.352
0.305
0.292
0.287
0.245
0.347
0.299
0.252
ACONE
macro-FI
ACC
0.110
0.225
0.458
0.352
0.335
0.422
0.436
0.275
0.463
0.279
0.359
0.292
0.231
0.351
0.388
0.434
0.360
0.265
0.291
0.378
ASN
ACC
0416
0.41
0.335
0.452
0.433
0.368
0.289
0.435
0.334
0.372
macro-F1
0,232
0.387
0.219
0.251
0,248
0.297
0.216
0.226
0.258
0.270
SpecReg
ACC
0.425
0.501
0.363
0.419
0.424
0.387
0.328
0.429
0.330
0.371
macro-F]
0,247
0.380
0.273
0.263
0,236
0.282
0.213
0.371
0.279
0,303
PA
macro-F1
ACC
0,236
0.402
0.331
0.529
0.295
0.351
0.243
0.323
0.416
0.229
0.299
0.357
0.306
0.238
0.367
0.317
0.336
0.286
0.357
0.263
GraphATA
ACC
0.407
0.518
0.382
0.341
0.327
0.414
0.351
0.341
0.341
0.321
macro-F]
0.241
0.339
0.287
0.252
0.291
0.289
0.234
0.351
0.274
0.254
HGDA
0417
0.529
0.421
0:361
0.337
0.317
macro-F1
0.239
0.333
0.321
0.251
0.284
0.279
0.212
0.344
0.271
0.249
0.535
0.381
SAGA
macro-F1
0.341
0.426
0.372
0.415
0.426
0.401
0.378
0.397
0.320
0.350
Table Cross-network node classification on MAG datasets
where i/ the classification prediction for the i-th node in the target graph of- Finally, by combining L. Lis, LD and Lr,, the overall loss function of our model can be represented as:
f L + ALIA (1 acf + BLs + 8LT
(16)
where a, 8 and 8 are trade-off parae--arameters... The parameters of the whole framework are updated via backpropagation.
EXPERIMENT
5.1 DATASETS
We use real-world citation network datasets for our experiments to demonstrate the effectiveness of our proposed algorithm for multi-view graph-domain adaptation the node classification task. Specifically, evaluate ACM1 (Shen et al., 2024a), ACM2 (Shen et al., 2024a), and MAG (Shen et al., 2024a) to test how well the method transfers across views and domains while preserving node-class information.
In these graphs, nodes correspond to papers and edges denote citation relationships between papers. The edges are defined by two veta--pths:: PAP (paper-authon paperr)pprrrrr and PSP subjecttpptetpppt for ACMI and ACM2, and PAP
author-paerr) and PP (paper-paper for MAG.
The ACMI and ACM2 graphs are labeled into three classes: database, wireless communication. and data mining. As the subset of OGBN-MAG (Wang et al., 2020), MAG is partitioned into six country. From these country
subgraphs(CN, US, DE, FR, JP, and RU), each corresponding subgraphs, we extract the ten most frequent classes. Any two of these country subgraphs can be used the source and target datasets in the multi-view graph node classification experiment. Details of the experimental datasets' statistics are provided in Table Appendix B.
5.2 BASELINES
In this work, we compare a representative set baselines for multi-view graph domain adaptation (GDA) in node classification. We begin with non-adaptive methods, where GCN (Kipf & Welling, 2016) provides a strong single-view baseline by performing standard graph convolution without any domain adaptation, and GAT (Veličković et al., 2017) offers an attention- aon-adaptive alternative that leverages expressive neighbor aggregation. AdaGCN (Dai et al., 2022) follows adaptive propagation method that adjusts message passing to node-level edge-level cues,
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informing how learning dynamics respond to varying neighborhood structures in multi-view setting. UDAGCN (Wu et al., 2020) represents a dual-graph convolutional-netwwen component learning framework for unsupervised GDA, capturing knowledge from local and global levels and using adversarial training to align representations across views. GRADE-N (Wu et al., 2023a) introduces graph subtree discrepancy quantify distribution shifts between source and target graphs, enabling principled evaluation of domain-aware transfer in multi-view contexts. co.texxss
(Shen et al., 2020a) is an adaptation- oriented method designed to align and fuse rep- resentations across views through cross-view ob- jectives, contributing to robust cross-view trans- fer, while ASN (Zhang et al., 2021) leverages domain-specific features within each network to extract domain-invariant shared representations across multiple networks. SpecReg (You et al., 2022) realizes performance improvements via reg- ularization inspired by the intersection of optimal transport domain adaptation and graph- the- ory, thereby aiding cross-view generalization under distribution shifts. Finally, PA (Liu et al., 2024) mitigates distribution shifts in graph data by recal- ibrating edge influences to address structural shifts and by adjusting classification losses tackle label shifts, enhancing cross-view robustness. Aggregate
ACDNE
ACMI-
ACM2-+ACMI
Methods
ACC
F1
ACC
F1
GCN
0.367
0.253
0.359
0.236
GAT
0.373
0.319
0.363
0.241
AdaGCN
0.431
0.272
0.386
0.362
UDAGCN
0.452
0.283
0.409
0.347
GRADE-N
0,469
0.291
0.373
0,343
ACDNE
0,457
0.261
0.392
0.363
ASN
0.436
0.321
0.437
0.327
SpecReg
0.493
0.308
0.431
0.381
PA
0.506
0.333
0,452
0.377
GraphATA
0,501
0.322
0.401
0,395
HGDA
0.511
0,311
0.441
0.414
SAGA
0.523
0.351
0,454
0.444
Table 2: Experiment on ACM datasets.
to Adapt (GraphATA) (Zhang& & He, 2025a) performs node-wise personalized aggregation of source GNNs for multi-source-free unsupervised graph domain adaptation, achieving state-of-the-ar performance across benchmarks. HGDA (Fang et al., 2025b) is a framework that mitigates cross- domain discrepancies by explicitly aligning homophilic, heterophilic, and attribute signals through specialized filters, thereby improving node classification across source and target graphs.
5.3 EXPERIMENTAL SETUP
Our multi-view graph setup comprises multiple views for each domain (source/target). To ensure fair comparison, all baselines evaluated under a consistent training/evaluation protocol, with explicit handling of single-view baselines in multi-view context as described below.
In multi-view settings, baseline handling as follows. For non-adaptation baselines (e.g.. GCN (Kipf & Welling, 2016), GAT (Veličković et al., 2017)), each view in the source domain is processed independently according to original formulation to obtain per-view embeddings; we then average the embeddings across all source views and train a GNN using these averaged embeddings with the available labels. For the target domain, we compute each view's embedding independently using the method described in the respective paper, average them, and input this averaged embedding to the trained GNN for node classification. This approach preserves the core idea of the original non-adaptive methods while leveraging cross-view aggregation for transfer. For adaptation baselines (e.g., AdaGCN (Dai et al., 2022), UDAGCN (Wu et al., 2020), GRADE-N (Wu et al., 2023a), ACDNE (Shen et al., 2020a), ASN (Zhang et al., 2021), SpecReg (You al., 2022), PA (Liu et al., 2024)), we compute view-specific embeddings for each source view, then average the embeddings across all source views obtain single representative source embedding for downstream inference on the target domain. In the target domain, target-view embeddings are computed according to the method's formulation and, if required by the baseline, are aligned or regularized via the adaptation mechanism during standard measures for classification problems.
and macro-F1 score are used as evaluation metrics, as they are
5.4 RESULTS AND ANALYSES
The results of experiments are summarized in Table and Table 2, where the best performance is highlighted in bold. A clear observation from the results is that our proposed SAGA method achieves the highest accuracy and macro-Fi scores across all listed source-target domain pairs, demonstrating its superior performance for multi-view graph domain adaptation in node classification compared
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Published as a conference paper at ICLR 2026
ACM2
ACM2-+ACMI
CN-+US
US-+CN
Methods
Methods
ACC
F1
ACC
F1
ACC
F1
ACC
F1
SAGApsP SPAP
0.366
0.314
0.358
0.296
SAGApP iPAP
0.316
0.183
0.318
0.276
S.AAAAA--PAP
0.379
0.319
0.363
0.283
SAGAPAP-PAP
0.329
0.191
0.346
0.318
S.A.APSP-PPPP
0.351
0.339
0.373
0.291
SAGApP-PP
0.335
0.177
0.323
0.297
SAGAPAP >PSP
0.351
0.332
0.389
0.302
SAGAPAP
0.321
0.230
0.349
0.321
SAGA
0.523
0.351
0.454
0.443
SAGA
0.553
0.341
0.535
0.426
Table 3: View Ablation studies on ACM and MAG datasets.
baseline methods. The unsatisfactory performance of GCN (Kipf & Welling, 2016)and GAT (Veličković et al., 2017) in this task indicates that merely transferring knowledge learned from the source domain to the target domain is inadequate for domain adaptation, underscoring the crucial importance of achieving proper alignment between source and target domains. While other adaptation methods demonstrate improvements over non-adaptive methods, they still fall significantly short of SAGA, suggesting that effectively analyzing and leveraging the relationships and information embedded within multiple views multi-view graphs is essential for superior performance. In addition, we present analyses of model efficiency and hyperparameter sensitivity in Appendix C. Specifically, although our approach requires computing Nr x Nr pairwise distances, these terms do not participate in backpropagation, thereby avoiding the need to propagate gradients through all distance computations. Consequently, while the method introduces some computational overhead, the overall complexity remains practically acceptable. We further conduct sensitivity studies on the hyperparameters a, 3, 5 in Appendix F, and K, showing that SAGA maintains stable performance under varying configurations, confirming the robustness of the model.
5.5 VIEW ABLATION STUDY
We evaluate the contribution of each relational view by restricting SAGA to single-view training, while keeping all other settings unchanged. The subscripts denote the source starget views used by each SAGA variant; e.g., SAGAPsP- PAP aligns representations from PSP to PAP. On ACM1/ACM2 we consider PAP (paper-author-paper) and PSP (paper-subject-papee)) PP (paper-paper). As reported Table 3, every restricted variant yields noticeably lower ACC/F1 than the full SAGA. which aggregates and co-aligns all available views. The gaps sizable (e.g.. on ACMI-ACM2. SAGA improves ACC by 0.21 and Fl by 0.10 over the best single/cross- -iew setting). These results indicate that each view encodes complementary semantics that PAP captures author/co-authorship proximity, PSP captures topical similarity, and PP captures paper-level linkage and leveraging them jointly necessary to attain the best cross-domain transfer performance.
MAG we consider PAP and
5.6 MODEL ABLATION STUDY
To validate the design choices of our proposed SAGA framework, we conduct a comprehensive ablation study evaluating the contribution of each key component. All experiments are performed on the ACMI and ACM2 datasets under the multi-view domain adaptation setting. We compare the following variants of our method:
SAGA w/o DVA: Remove the Dominant View Alignment module (LiA Lcx). Replace alignment by the DANN method.
SAGA w/o Dynamic Hop: Fix the hop number K across all views instead of dynamically updating it.
SAGA w/o Dominant View: Replace the dominant view selection with a simple averaging of all views.
SAGA (Full): Our complete framework with all components.
Fig. 4 demonstrates the essential contributions of each SAGA component. The dominant view alignment module (LDv)) is most critical, with its removal causing the largest performance drop. Dynamic hop selection significantly outperforms fixed-hop aggregation, confirming the need for

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Thears 13540AJJU
30
50
Aucuraity
45-
40-
Accurac
10
Accurasc
45
40-
35
15
60
35
30
40
35
30
25
90
(a) +
A2
(b)
(c) CN ++
(d) CN
Figure Classification accuracy of different variants of SAGA on ACM and MAG dataset
view-specific depth adaptation. Simply averaging views without identifying the dominant anchor also reduces performance. The full SAGA framework integrates these components, which collectively address structural discrepancies in multi-view graph domain adaptation.
CONCLUSION
In this paper, we propose the SAGA framework to address the multi-view graph domain adaptation problem cross-domain node classification. We reveal the importance of multi-view structural alignment through SAD in empirical analysis (Zeng et al., 2024c;b;a; Pu et al., 2025). Our approach dynamically aggregates and aligns multi-view graph structures by optimizing cross-hop and cross- view structural consistency. Comprehensive experiments multi-rclational graph benchmarks verify the superiority of our approach. We will also delve deeper into multi-view graph domain adaptation theory to develop more powerful and generalizable models in many other relvent aeras. (Jing et al., 2025; Yu et al., 2025b; Jing et al., 2026b;a; Yu et al., 2025a).
ACKNOWLEDGMENTS
This work supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants program.
REFERENCES
Yiming Chen, Shiji Song, Shuang Li, and Cheng Wu. A graph embedding framework for maximum mean discrepancy-basee based domain adaptation algorithms. IEEE Transactions Image Processing, 29:199-213, 2019.
Eli Chien, Jianhao Peng. Pan Li, and Olgica Milenkovie. Adaptive universal generalized pagerank graph neural network. In International Conference on Learning Representations, 2020.
Quanyu Dai, Xiao-Ming Wu, Jiaren Xiao, Xiao Shen, and Dan Wang. Graph transfer learning via adversarial domain adaptation with graph convolution. IEEE Transactions Knowledge and Data Engineering. 35(5):4908-499) 4922, 2022.
Ruiyi Fang, Liangjian Wen, Zhao Kang, and Jianzhuang Liu. Structure preeeevvng graph represen- tation learning. In 2022 IEEE International Conference on Data Mining (ICDM), pp. 927-932.
IEEE, 2022.
Ruiyi Fang, Bingheng Li, Zhao Kang, Qiuhao Zeng, Nima Hosseini Dashtbayaz, Ruizhi Pu, Boyu Wang, and Charles Ling. On the benefits of attribute-die driven graph domain adaptation. In The Thirleenth International Conference Learning Representations, 2025a.
Ruiyi Fang, Bingheng Li, Jingyu Zhao, Ruizhi Pu, Qiuhao Zeng. Gezheng Xu, Charles Ling, and Boyu Wang. Homophily enhanced graph domain adaptation. ICML., 2025b.
Ruiyi Fang, Shuo Wang. Ruizhi Pu, Qiuhao Zeng, Hao Zheng, Ziyan Wang, Jiale Cai, Zhimin Mei, Song Tang, Charles Ling, et al. Graph domain adaptation via homophily-agnosti reconstructing structure. AAAI, 2026.
10

---

Published as a conference paper at ICLR 2026
Gaoyang Guo, Chaokun Wang, Bencheng Yan, Yunkai Lou, Hao Feng, Junchao Zhu, Jun Chen, Fei He, and Philip Yu. Learning adaptive node embeddings across graphs. IEEE Transactions on Knowledge and Data Engineering, 2022.
Hui Guo, Grace Y Yi, and Boyu Wang. Addressing both variable selection and misclassified responses with parametric and semiparametric methods. Bernoulli, 32(2):1303-137,, 2026.
Jincheng Huang, Lun Du, Xu Chen, Qiang Fu, Shi Han, and Dongmei Zhang. Robust mid-pass filtering graph convolutional networks. In Proceedings of the ACM Web Conference 2023, pp. 328- 338, 2023.
Jincheng Huang, Yujie Mo, Ping Hu, Xiaoshuang Shi, Shangbo Yuan, Zeyu Zhang, and Xiaofeng Zhu. Exploring the role of node diversity directed graph representation learning. In Proceedings of the Thirly-Third International Joint Conference on Artificial Intelligence, 2024a.
Jincheng Huang, Jialie Shen, Xiaoshuang Shi, and Xiaofeng Zhu. On which nodes does gen fail? enhancing gen from the node perspective. In Forty-first International Conference on Machine Learning, 2024b.
Jincheng Huang, Yujie Mo, Xiaoshuang Shi, Lei Feng. and Xiaofeng Zhu. Enhancing the influence of labels on unlabeled nodes in graph convolutional networks. In Forty-second International Conference on Machine Learning, 2025a.
Jincheng Huang, Jie Xu, Xiaoshuang Shi, Ping Hu, Lei Feng, and Xiaofeng Zhu The final layer holds the key: unified and efficient gnn calibration framework. arXiv preprint arXiv:2505.1133 2025b.
Zhong Ji, Zhihao Li, Yan Zhang, Haoran Wang, Yanwei Pang. and Xuelong Li. Hierarchical matching and reasoning for multi-query image retrieval. Neural Networks, 173: 106200, 2024.
Zhong Ji, Zhihao Li, Yan Zhang, Yanwei Pang, and Xuelong Li. Visual semantic contextualization network for multi-query image retrieval. IEEE Trunsactions on Multimedia, 27:7067 7080, 2025.
Taotao Jing, Bingrong Xu, and Zhengming Ding. Towards fair knowledge transfer for imbalanced domain adaptation. IEEE Transactions on Image Processing, 30:8200-888 12100008811,2000 2021.
Zihao Jing, Yan Sun, Yan Yi Li, Sugitha Janarthanan, Alana Deng, and Pingzhao Hu. Structure- aware fusion with progressive injection for multimodal molecular representation learning. In The Thitt Annual Conference on Neural Information Processing Systems, 2025.
Zihao Jing, Yuxi Long, and Ganlin Feng. Pruning for generalization: A transfer-oriented spatiotem- poral graph framework. arXiv preprint STt::2602.04153. 2026a.
Zihao Jing, Qiuhao Zeng, Ruiyi Fang, Yan Sun, Boyu Wang, and Pingzhao Hu. Entropy- guided dynamic tokens for graph-llm alignment in molecular understanding. arXiv preprint arXiv:2602.02742, 2026b.
Thomas N Kipf and Max Welling, Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02900 2016.
Bingheng Li, Xuanting Xie, Haoxiang Lei, Ruiyi Fang, and Zhao Kang. Simplified penet with robustness. Neural Networks, 184:107099, 2025a.
Zhihao Li, Jiale Cai, Gezheng Xu, Hao Zheng. Qiuyue Li, Fan Zhou, Shichun Yang, Charles Ling, and Boyu Wang. Versatile transferable unlearnable example generator. The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025b.
Shikun Liu, Deyu Zou, Han Zhao, and Pan Li. Pairwise alignment improves graph domain adaptation. arXiv preprint arXiv:2403.0100 2024.
Haitao Mao, Lun Du, Yujia Zheng, Qiang Fu, Zelin Li, Xu Chen, Shi Han, and Dongmei Zhang Source free unsupervised graph domain adaptation. The 17th ACM International Conference on Web Search and Data Mining, 2024.
11

---

Published as a conference paper at ICLR 2026
Erxue Min, Hsiu- Yuan Huang, Xihong Yang. Min Yang, Xin Jia, Yunfang Wu, Hengyi Cai, Junfeng Wang, Shuaiqiang Wang, and Dawei Yin. From prompting to alignment: A generative framework for query recommendation. arXiv preprint arXivv2504.10008 2025.
Yujie Mo. Yuhuan Chen, Yajie Lei, Liang Peng, Xiaoshuang Shi, Changan Yuan, and Xiaofeng Zhu. Multiplex graph representation learning via dual correlation reduction. IEEE Trunsactions on Knowledge and Data Engineering, 2023a.
Yujie Mo, Yajie Lei, Jialie Shen, Xiaoshuang Shi, Heng Tao Shen, and Xiaofeng Zhu. Disentangled multiplex graph representation learning. In International Conference on Machine Learning, pp. 24983-25005. PMLR, 2023b.
Erlin Pan and Zhao Kang. High-order multi-view clustering for generic data. Information Fusion, 100:101947, 2023.
Ruizhi Pu, Gezheng Xu, Ruiyi Fang, Bing-Kun Kun Bao, Charles Ling, and Boyu Wang. Leveraging group classification with descending soft labeling for deep imbalanced regression. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 19978-19985, 2025.
Ziyue Qiao, Xiao Luo, Meng Xiao, Hao Dong, Yuanchun Zhou, and Hui Xiong. Semi-supervised domain adaptation in graph transfer learning. IJCAI, 2023.
Meng Qu, Jian Tang, Jingho Shang, Xiang Ren, Ming Zhang, and Jiawei Han. An attention based collaboration framework for multi-view network representation learning. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1767-1776, 2017.
Xiao Shen and Fu Lai Chung. Network embedding for cross-network node classification. arXiv preprint 1rxiv:1901 0.0264,, 2019.
Xiao Shen, Quanyu Dai, Fu-lai Chung, Wei Lu, and Kup-Sze Choi. Adversarial deep network embedding for cross-network node classification. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pp. 2991- 2020a.
Xiao Shen, Quanyu Dai, Sitong Mao, Fu-lai Chung, and Kup-Sze Choi. Network together: Node classification via cross-network deep network embedding. IEEE Transactions on Neural Networks and Learning Systems, 1935): 2020b.
Xiao Shen, Mengqiu Shao, Shirui Pan, Laurence T Yang, and Xi Zhou. Domain-adaptiie graph antention-supeeviieed network for cross-network edge classification. IEEE Transactions on Neural Networks and Learning Systems, 35(12):17842-1785 2023.
Xiao Shen, Zhihao Chen, Shirui Pan, Shuang Zhou, Laurence T Yang, and Xi Zhou. Open-set cross-network node classification via unknown-excluded adversarial graph domain alignment. In Proceedings of the AAAI Conference Artificial Intelligence, volume 39, pp. 20398 80398-20408, 2025.
Zhixiang Shen, Haolan He, and Zhao Kang. Balanced multi-relational graph clustering. In Proceed- ings of the 32nd ACM International Conference on Multimedia, pp. 4120-4128, 2024a.
Zhixiang Shen, Shuo Wang, and Zhao Kang. Beyond redundancy: Information-awan unsupervised multiplex graph structure learning. Advances in neural information processing systems, 37: 31629-31658 31658, 2024b.
Boshen Shi, Yongqing Wang, Fangda Guo, Jiangli Shao, Huawei Shen, and Xueqi Cheng. Improving graph domain adaptation with network hierarchy. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 2249-2225 2258, 2023.
Boshen Shi, Yongqing Wang, Fangda Guo, Bingbing Xu, Huawei Shen, and Xueqi Cheng. Graph domain adaptation: Challenges, progress and prospects. arXiv preprint arXiv:2402.00904, 2024.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. arXiv preprint arXiv: 10. /0903, 2017.
Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, Yuxiao Dong, and Anshul Kanakia. Microsoft academic graph: When experts are not enough. Quantitative Science Studies, 1(1): 396-413, 2020.
12

---

Published as a conference paper at ICLR 2026
Qin Wang, Olga Fink, Luc Van Gool, and Dengxin Dai. Continual test-time domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp- 7201-7211, 2022.
Shuo Wang, Shunyang Huang, Jinghui Yuan, Zhixiang Shen, and Zhao Kang. Cooperation of experts: Fusing heterogeneous information with large margin. arXiv preprini arXiv:2505.20853: 2025a.
Shuo Wang, Bokui Wang, Zhixiang Shen, Boyan Deng, and Zhao Kang. Multi-domain graph foundation models: Robust knowledge transfer via topology alignment. ICML, 2025b.
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. Sim- plifying graph convolutional networks. In International conference machine learning, pp. 6861-6871. PMLR, 2019.
Jun Wu, Jingrui He, and Elizabeth Ainsworth, Non-iid transfer learning on graphs. In Proceedings of the AAAI Conference Artificial Intelligence, volume 37, pp. 10342-10350, 2023a.
Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, Bo Long, et al. Graph neural networks for natural language processing: A survey. Foundations and Trends@ in Machine Learning, 16(2):119-33) 2023b.
Man Wu, Shirui Pan, Chuan Zhou, Xiaojun Chang, and Xingquan Zhu. Unsupervised domain adaptive graph convolutional networks. In Proceedings of The Web Conference 2020, pp. 1457-1467, 2020.
Feng Xia, Ciyuan Peng, Jing Ren, Falih Gozi Febrinanto, Renqiang Luo, Vidya Saikrishna, Shuo Yu, and Xiangjie Kong. Graph learning. Foundations and Trends@ in Signal Processing, 19(4): 371-551, 2025.
Xuanting Xie, Wenyu Chen, and Zhao Kang. Robust graph structure learning under heterophily. Neural Networks, 185:107206, 2025a.
Xuanting Xie, Bingheng Li, Erlin Pan, Zhaochen Guo, Zhao Kang, and Wenyu Chen. One node one model: Featuring the missing-half for graph clustering. In Proceedings of the AAAI Conference on 2025b.
Artificial Intelligence, volume 39, pp.
Gezheng Xu, Hui Guo, Li Yi, Charles Ling, Boyu Wang, and Grace Yi. Revisiting source-free domain adaptation: A new perspective via uncertainty control. In The Thirteenth International Conference on Learming Representations, 2025a.
Gezheng Xu, Li Yi, Pengcheng Xu, Jiaqi Li, Ruizhi Pu, Changjian Shui, Charles Ling, A Ian McLeod, and Boyu Wang. Unraveling the mysteries of label noise in source-free domain adaptation: Theory and practice. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025b.
Jie Xu, Huayi Tang, Yazhou Ren, Liang Peng, Xiaofeng Zhu, and Lifang He. Multi-level feature learning for contrastive multi-view clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition (CVPR), pp. 16051-16060, 2022a
Jie Xu, Shuo Chen, Yazhou Ren, Xiaoshuang Shi, Hengtao Shen, Gang Niu, and Xiaofeng Zhu. Self- weighted contrastive learning among multiple views for mitigating representation degeneration. In Advances in Neural Information Processing Systems (NeurlPS), volume 36, pp. 1119-1131, 2023a.
Pengcheng Xu, Boyu Wang, and Charles Ling. Class overwhelms: Mutual conditional blended-target domain adaptation. Proceedings of the AAAI Conference on Artificial Intelligence. 2023b.
Zihao Xu, Hao He, Guang- Lee, Yuyang Wang, and Hao Wang. Graph-relational domain adaptation. 2022b.
arXiv preprint
Bencheng Yan and Chaokun Wang. Graphae: adaptive embedding across graphs. In 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 1958-1961. IEFE, 2020.
Liang Yang, Zhenna Li, Jiaming Zhuo Jing Liu, Ziyi Ma, Chuan Wang, Zhen Wang, and Xiaochun Cao. Graph contrastive learning with joint spectral augmentation of attribute and topology. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 21983-21994 2025a.
13

---

Published as a conference paper at ICLR 2026
Liang Yang, Hui Ning, Jiaming Zhuo, Ziyi Ma, Chuan Wang, Wenning Wu, and Zhen Wang. Source-free graph foundation model adaptation via pseudo-source reconstruction. 2026.
Shiqi Yang, Yaxing Wang, Joost Van De Weijer, Luis Herranz, and Shangling Jui. Generalized source-free domain adaptation. In Proceedings of the IEEE/CVF International Conference on Computer Vision. pp.
2021.
Xihong Yang, Xiaochang Hu, Sihang Zhou, Xinwang Liu, and Zhu. Interpolation-basee contrastive learning for few-label superviie learning. IEEE Transactions Neural Networks and Learning Systems, 35(2):2054-250) 2065, 2022.
Xihong Yang, Jin Jiaqi, Siwei Wang, Ke Liang, Yue Liu, Yi Wen, Suyuan Liu, Sihang Zhou, Xinwang Liu, and En Zhu. Dealmve: Dual contrastive calibration for multi-view clustering. In Proceedings of the 31st ACM International Conference Multimedia, pp. 337-346, 2023a.
Xihong Yang, Yue Liu, Sihang Zhou, Siwei Wang, Wenxuan Tu, Qun Zheng, Xinwang Liu, Liming Fang, and En Zhu. Cluster-guided contrastive graph clustering network. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pp. 10834-10842, 2023b.
Xihong Yang, Cheng Tan, Yue Liu, Ke Liang, Siwei Wang. Sihang Zhou, Jun Xia, Stan Li, Xinwang Liu, and En Zhu. Convert: Contrastive graph clustering with reliable augmentation. In Proceedings of the 31 ACM International Conference on Multimedia, pp. 319-327 -27,,, 2023c.
Xihong Yang, Erxue Min, Ke Liang Yue Liu, Siwei Wang, Sihang Zhou, Huijun Wu, Xinwang Liu, and En Zhu. Graphlearner: Graph node clustering with fully Icarnable augmentation. In Proceedings of the 32nd ACM International Conference on Multimedia, pp. 5517-5526, 2024a.
Xihong Yang, Yiqi Wang, Yue Liu, Yi Wen, Lingyuan Meng, Sihang Zhou, Xinwang Liu, and En Zhu. Mixed graph contrastive network for semi-supervised node classification. ACM Transactions on Knowledge Discovery from Data, 2024b.
Xihong Yang, Siwei Wang. Jiaqi Jin, Fangdi Wang, Tianrui Liu, Yueming Jin, Xinwang Liu, En Zhu, and Kunlun He. Generalized deep nulti-view clustering via causal learning with partially aligned cross-view correspondence. In Proceedings of the IEEE/CVF International Conference on Com- puter Vision, 2025b.
Xihong Yang, Yiqi Wang, Jin Chen, Wenqi Fan, Xiangyu Zhao, En Zhu, Xinwang Liu, and Defu Lian. Dual test-time training for outtof-disttobution recommender system. IEEE Transactions Knowledge and Data Engineering, 376))333 2025c. doi: 19.1009K2.222225... .35481660
Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen. Graph domain adaptation via cheory-grounded spectral regularization. The Eleventh International Conference on Learning Representations, 2022.
Shuo Yu, Huafei Huang, Yanming Shen, Pengfei Wang. Qiang Zhang, Ke Sun, and Honglong Chen. Formulating and representing multiagent systems with hypergraphs. IEEE Transactions Neural Networks and Learning Systems, 36(3):4599-46133 2024.
Shuo Yu, Yicong Li, Shuo Wang. Tao Tang, Qiang Zhang, Jingjing Zhou, Ivan Lee, and Feng Xia. Cage: A causaliyyinnspieed graph neural network explainer for recommender systems. ACM Transactions on Information Systems, 43(6):1 2025a.
Shuo Yu, Yingbo Wang, Ruolin Li, Guchun Liu, Yanming Shen, Shaoxiong Ji, Bowen Li, Fengling Han, Xiuzhen Zhang, and Feng Xia. Graph2text or graph2token: A perspective of large language models for graph learning. ACM Transactions on Information Systems, 2025b.
Shuo Yu. Yingbo Wang. Zhitao Wan, Yanming Shen, Qiang Zhang, and Feng Xia. Path-aware few-shot knowledge graph completion. IEEE Transactions on Artificial Intelligence, 2025c.
Jingyang Yuan, Xiao Luo, Yifang Qin, Zhengyang Mao, Wei Ju. and Ming Zhang. Alex: Towards effective graph transfer learning with noisy labels. In Proceedings of the 31st ACM International Conference Multimedia, pp. 3647-3656. 2023.
14

---

Published as a conference paper at ICLR 2026
Qiuhao Zeng, Wei Wang, Fan Zhou, Charles Ling, and Boyu Wang. Foresee what you will learn: data augmentation for domain generalization in non-stationary environment. In Proceedings of the AAAl conference artificial intelligence, volume 37, pp. 11147-11155, 2023.
QIUHAO Zeng, Jerry Huang, Peng Lu, Gezheng Xu, Boxing Chen, Charles Ling, and Boyu Wang. Zeta: Leveraging z-order curves for efficient top-k attention. In The Thirteenth International Conference on Learning Representations, 2024a.
Qiuhao Zeng, Long- Kai Huang, Qi Chen, Charles Ling, and Boyu Wang. Towards understanding evolving patterns in sequential data. Advances in Neural Information Processing Systems, 37: 132747-132773, 2024b.
Qiuhao Zeng. Changjian Shui, Long-Kai Huang, Peng Liu, Xi Chen, Charles Ling, and Boyu Wang. Latent trajectory learning for limited timestamps under distribution shift over time. In The Twelfth International Conference on Learning Representations, 2024c.
Qiuhao Zeng, Jerry Huang, Peng Lu, Gezheng Xu, Boxing Chen, Charles Ling, and Boyu Wang. Zeta: Leveraging z-order curves for efficient top-k attention. ICLR, 2025.
Xiaowen Zhang, Yuntao Du, Rongbiao Xie, and Chongjun Wang. Adversarial separation network for cross-network node classification. In Proceedings of the 30th ACM International Conference Information & Knowledge Management, pp. 2618-2626, 2021.
Zhen Zhang and Bingsheng He. Aggregate to adapt: Node-centric aggregation for multi-source-free graph domain adaptation. In Proceedings of the ACM Web Conference 2025, pp. 2025a.
Zhen Zhang and Bingsheng He. Towards unsupervised open-set graph domain adaptation via dual reprogramming. The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025b.
Hao Zheng, Zhigang Hu, Liu Yang, Aikun Xu, Meiguang Zheng, Hui Xiao, and Keqin Li. Proactive spatio-temporal request prediction for replica placement in edge-cloud computing. IEEE Internet of Things Journal, 2025a.
Hao Zheng, Zhigang Hu, Liu Yang, Meiguang Zheng, Aikun Xu, and Boyu Wang. Confree: Conflict- free client update aggregation for personalized federated learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 22875-22888 2025b.
Hao Zheng, Zhigang Hu, Liu Yang, Meiguang Zheng, Aikun Xu, and Boyu Wang. Fedcalm: Conflict- aware wise mitigation for selective aggregation in deeper personalized federated learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp-
15444-15453 2025c.
Lei Zhu, Qi She, Qian Chen, Yunfei You, Boyu Wang, and Yanye Lu. Weakly supervised object localization as domain adaption. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14637-14646. 2022.
Jiaming Zhuo Ziyi Ma, Yintong Lu, Yuwei Liu, Kun Fu, Di Jin, Chuan Wang, Wenning Wu, Zhen Wang, Xiaochun Cao, et al. A closer look at graph transformers: Cross-aggregation and beyond. In The Thitt Annual Conference on Neural Information Processing Systems, 2025.
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Types
Datasets
#Node
#Relation
#Edge
#Feat
#Label
PAP
26.256
ACMI
3,025
512
3
PSP
2,207,736
ACM
PAP
57,853
ACM2
4,019
512
3
PSP
4,338,213
PAP 157,303
CN
25,467
128
10
PP
43,683
PAP
12,332,742
US
55,392
128
10
PP
289.833
PAP
41,165
JP
12,651
128
10
PP
20,747
MAG
PAP
51.739
FR
12,052
128
10
PP
30,915
PAP
77,100
17,919
128
PP 45,528
PAP
3,315
RU
3,690
128
10
PP
3,539
Table 4: Statistics of experimental datasets
A USE OF LARGE LANGUAGE MODELS (LLMs)
We declare that Large Language Models (LLMs) were used exclusively as auxiliary tools aid the writing of this paper, primarily for polishing, grammar checking, and improving readability. LLMs were not involved in conceptual design, theoretical formulation, experimental implementation, or result analysis. All research contributions, including methodology, experiments, and conclusions, were conceived, conducted, and validated entirely by the authors.
B DATASET EXPERIMENT DETAILS
The ACMI and ACM2 graphs are labeled into three classes: database, wireless communication, and data mining. the subset OGBN-MAG (Wang et al., 2020), MAG partitioned into six subgraphs(CN, US, DE, FR, JP. and RU), each corresponding to country. From these country subgraphs, we extract the ten most frequent classes. Any two of these country subgraphs can be used as the source and target datasets in the multi-view graph node classification experiment. Details of the experimental datasets' statistics are provided in Table 4.
C MODEL. EFFICIENT EXPERIMENT
Model Complexity: Here, we analyze the computational complexity of the proposed SAGA. The computational complexity primarily depends on the filter layers and key loss function. For given graph G, let N represent the total number of nodes in the graph, V represent view number and the feature dimension. For Scalable Graph Encoding fe: Includes matrix multiplication with O(N2 d). SAGA per-iteration complexity is dominated by multi-view propagation O(V N d), and a forward-only SAD computation ( 2 Combining these yields an overall complexity ofO (VN- d 22 d). where the quadratic SAD term does not incur gradient cost, making the practical training overhead similar to existing GDA methods. Thus, the total complexity of SAGA still: O2VN2 d). Note that HGDA is inherently single- method. we extend HGDA to the multi-view MGDA setting, it would inherit the same computational overhead, resulting in complexity of O(V 2 d). Therefore, although SAGA incorporates dynamic multi-view selection, its computational overhead remains comparable to that of HGDA; the methods do not differ by orders of magnitude asymptotic complexity.
Model Efficient Experiment: To further investigate the efficiency of SAGA, Table4 reports the rumning time comparison across various algorithms. We also compared the training time and GPU memory usage of common baselines UDAGCN and recent SOTA method, ASN, and HGDA. As
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Dainset
Method
Training Time (Normalized W.F. UDAGCN)
Memory Usage (Narmalized wizz. UDAGCN)
Accuracy(9))
UDAGCN
ASN
1.314
1.14
0.436
ACMI-AAMM
HGDA
0.914
1.124
0.521
SAGA
1.163
1.112
0.793
UDAGCN
1
0.425
ASN
1,413
1.513
0.402
CN +US
1.011
1.103
0.402
HODA
1.007
1.173
0.417
SAGA
L,109
1,058
UDAGCN
0.510
ASN
1.311
1.173
0.569
PA
L.002
1,097
0.562
B-E
1,051
SAGA
L148
1,107
0.573
Table 5: Comparison of Training Time, Memory Usage, and Accuracy on ACM and MAG datset.
shown in Table 5, the evaluation results on ACM dataset further demonstrate that method achieves superior performance with tolerable computational and storage overhead.
(a) HGDA
(b) UDAGCN
(c) PA
(d) ASN
(e) SAGA
Figure 5: t-SNE visualization of node embeddings MAG datasets.
(a) only PAP
PSP
(c) both PAP and PSP
Figure 6: t-SNE visualization of node embeddings on ACMI and ACM2 datasets. (a) Embeddings using only the PAP view; (b) Embeddings using only the PSP view; (c) Embeddings using the joint PAP and PSP views. Each color represents distinet class, with lighter and darker shades indicating source and target domain nodes within the same class, respectively.
D VISUALIZATION
To evaluate the quality of learned representations, we visualize the node embeddings generated by SAGA using t-SNE on both ACMI and ACM2 datasets. We compare three different configurations: (a) embeddings using only the PAP structural view, (b) embeddings using only the PSP structural view, and (c) embeddings incorporating both PAP and PSP views (joint representation).
As shown in Fig.6, each color represents distinct node class, while within each class, lighter and darker shades denote nodes from the source and target domains, respectively. All three configurations achieve clear separation between different classes, demonstrating that SAGA effectively preserves inter-class discriminability.
However, significant differences emerge in the alignment of cross-domain nodes within the same class. In subfigures (a) and (b), where only single views are used, source and target domain nodes of the same class exhibit noticeable distribution gaps. In contrast, subfigure (c) shows substantially
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improved overlap between domains within each class, indicating that the joint representation better aligns the source and target distributions.
These results suggest that integrating multiple structural views provides complementary information that enhances domain adaptation performance. The multi-view approach not only maintains class discrimination but also facilitates better cross-domain alignment, ultimately yielding transferable node representations for classification tasks.
To further demonstrate the advantage of our proposed method, also visualize the embedding results on MAG generated by competitive methods HGDA, UDAGCN, PA, and ASN, which are shown in Fig.5. Our SAGA method produces compact alignment structure. In other words, method has the best domain distance between source target and the most distinct boundaries between different classes.
E DESCRIPTION OF ALGORITHM SAGA
Algorithm 1:
The proposed algorithm SAGA
Input:
Source node feature matrix X source original graph adjacency matrix A Target node feature matrix X"; Target graph adjacency matrix A source node label matrix ys; maximum number of iterations 7)
Compute the SAD* between source and target graph by running Formula 3 algorithm. for to do
z S.Ks T.Kr Kr Encoder (A \/ embedding of source and target graph
= Encoder(A As,s X
Z
X = Decoder Z S.Ks)
Vs
X = Decoder (ZV ///KT/// decoding of source and target graph
Lto alleviate representation collapse
SAD min,,K SAD is calculated once per epoch
S. and TT.Kr through intra-view alignment by Formula 11 algorithm.
7 Ks and V zT.KKr through cross-view alignment by Formula 13 algorithm.
Domain adaptive between S and ST // adaptive in both cross and intra views
0f constrained byu and o! constrained byø?
Calculate the overall loss with (16)
Update all parameters of the framework according the overall loss
end
Predict the labels of target graph nodes based on the trained framework.
Output: Classification result y
F PARAMETER ANALYSIS
is chosen from the set {0.05, 0.25, 0.5, 0.75, 0.95) while 3 and 8 are chosen from the set (0.005, 0.01, 0.1, 0.5, 1.5}. These values provide flexibility for adjusting the relative importance of different loss terms. K (the number of neighbors order) selection typically K E (1,.... 10] The optimal value for & depends on the density and connectivity of the graph. Due extremely large k could also introduce noisy that will deteriorate the performance. Usually our largest & will be
The ACM dataset typically exhibits fewer nodes but complex edge structures. Due to its sparsity, relatively larger values of a, 3, and are employed to enhance topology alignment and capture essen- tial structural relations, with parameter 3 selected from (0.1, 0.5) and selected from (0.5, 0.25). Moreover, smaller neighborhood orders are preferred, and l chosen from 3, 4 accordingly.
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 have significant impact the values of or,3 and 8, the majority of which selected
MAG Dataset:
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Types
Datasets
8
ACMI-ACM2
0.5
0.5
0.01
ACM
ACM2-+ACMI
0.25
0.1
0.01
CN->US
0.5
0.1
0.1
5
US-CN
0.25
0.1
0.1
5
JP-CCN
0.25
0.5
0.1
CN-+JP
0.25
0.1
0.5
DE-FR
0.25
0.1
0.1
MAG
0.1
0.01
RU-UUS
0.25
0.1
0.1
US-RU
0.25
0.1
0.01
DE-CN
0.5
0.1
0.1
CN DE
0.25
0.01
0.1
Table 6: Experiment hyperparameter setting Value.
from the set {0.1, 0.25, 0.5). The parameter It works well in this context, enabling the model to capture high-level local and global structural information within the graph. We select from {4,
In this section, analyze the sensitivity of the parameters of method the ACM dataset and MAG dataset. As shown in Figure.7 in each subfigure, the accuracy usually peaks at - 3 with k. This is reasonable since increasing k 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 a unique optimal value for k. determined by its inherent properties. From Figure.8 subfigures for a, 8, and 8, we can see SAGA has competitive performance on a large range of values, which suggests the stability of our method.
0.55
0.60
0.50
0.55
Accurapy
0.45
Accuracy
0.50
0.35
0.40
CM(PP) US\P9)
A1(PSP)
A2(PSP)
CH/FF) US:PAP)
0.30
AI(PSP) A2(PAP)
0.35
GN/MAP)
AT[PA] A2(PS))
CH/PAP) US/PAP!
0.25
0.30
1
2
3
5
7
8
9
10
2 3
8
9
10
parameter
parameter
(a) Al A2
(b) CN US
0.60
0.50
0.55
0.45
0.50
Accuracy
0.40
Accuraccy
0.45
0.35
0.40
DE(PP) FR/PP)
0.30
US/PP) RU(PP)
DE(PP) FRPPP)
USIP) RU/PAP)
DE(PAP) FR(PP)
US/PAP: RU(P))
DE(PAPI FR(PAP)
US/PAP: RU/PAP:
0.30
0.20
3
5
6
9
10
5
6
10
(c) DE + FR
(d) US - RU
Figure 7: The influence of parameters i on ACM and MAG datasets.
G EXPERIMENT DETAIL OF EMPIRICAL STUDY
We evaluate the proposed SAGA framework under a unified multi-view graph domain adaptation protocol on both the ACM and MAG benchmarks. To quantify the performance gap between the
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56
Accuracy
50
50
46
Accuracy
8
40
8
6.1
18
0.01
0.01
86
0.00
0005
0.005
0.9
0,009
8.1
0.5
(a) A1 A2
(b) A1 A2
(c) A1 A2
45
Accuracy
0.5
06
80
05
0.1
0.1
&
0.005
dat
025
0.5
8.78
0.8
cas
91
8
(d)
(e) A2
Al
A2
A1
Figure 8: The influence of parameters a. 3, and on ACM datasets.
source and target domains, we compute L, Lr Specifically, Lg follows the definition in Equation 14, where Lu does not correspond to the target-domain classification loss in Equation 17. The latter is defined
êT (Jr (7²)
Σ v log (i
(17)
NT
In this context, the difference L, Lx serves as a measure of the relative performance between the source and target domains.
H FURTHER ANALYSIS OF SAD
Proposition (Bound for Di A) (Fang al., 2025a)). For any 7 2 and under the assumption that the prior distribution P over the classification function family H is defined, establish a bound for the domain discrepancy measure D (P: PPP)) A). Specifically, have the following inequality:
(PPA) 5o
Σ Σ - iEVS JEVT
x 11
Σ Σ REVS JEVT
|X - -x,11
(18)
Experiment Setting: To assess the cross-domain structural mismatch, we compute two com- plementary discrepancy measures. First, following the procedure described in the GAA frame- work Fang et al. (2025a), we evaluate the structural bound value by calculating the first-term deviation [[ (A" 112 2 as outlined in Appendix F of GAA. This provides fixed-hop, first-order structural discrepancy between source and target graphs. Second, we report the Structural Aggregation Distance (SAD) used in SAGA, defined in Eq. (3). SAD computes the Frobenius-norm divergence between source and target embeddings after k-hop structural aggregation and is dynamically up- dated during training. For each epoch, SAD evaluated across all view-hop combinations, and the minimum value is selected as the dominant structural signal. All experiments adopt the same multi-view MGDA protocol as the main results, using the standardized ACM and MAG splits, shared encoder-decoder architecture, and identical training hyperparameters, SAD is recalculated once per epoch using the current embeddings without additional backpropagation cost, ensuring a consistent and fair comparison with the static GAA bound.
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B05--0 2000
jeeL-se 19100
Jan -000
BAD
0.2
BAD
06
64
Speck
Reach
Specs
(a) Al - A2
(b) CN US
(c) JP - CN
Jolo voral
0.
06
84
SA0
SAO
00
2.0
00
02
Tpech
(d) DE FR
(e) CN DE
(l) US -> RU
Figure 9: Training dynamics of SAGA across six domain-shift settings. The x-aaxs denotes training epochs; the left y-axis reports the squared feature discrepancy [| X (A" , used in the GAA bound, while the right y-axis shows the Structural Aggregation Distance (SAD) defined in Eq. (3). Each subplot contains four trajectories: the GAA structural discrepancy, its smoothed variant, the dynamically updated SAD, and its smoothed version. Across all scenarios, both SAD curves remain consistently and substantially lower than the GAA-basee discrepancies, demonstrating that SAD provides tighter and more stable measure of cross-domain structural mismatch. Moreover, SAD decreases monotonically and converges during training, whereas GAA curves remain high and fluctuate. These results confirm that SAGA's dynamic SAD offers more reliable alignment signal, leading to superior adaptation performance compared to the fixed-hop hop GAA discrepancy.
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To more comprehensively evaluate the effectiveness of the proposed Structural Aggregation Dis- tance (SAD), we conduct an additional experiment comparing SAD against the structural term used in the GAA bound. The GAA discrepancy measures pairwise deviations of the form [|(4 x ); (A"xT), 111,2 capturing only the first-hop aggregated mismatch between source and target nodes. Although both SAD and the GAA term quantify how structural aggregation influence node representations, the GAA estimator is fundamentally fixed: it is tied to a predetermined hop depth and cannot update training progresses. In contrast, SAD is explicitly designed as a dynamic view-hop discrepancy measure that recomputes structural differences across all view-hop combi- nations and selects the dominant pair adaptively at each epoch. This distinction is essential. As illustrated in Fig. ??, the dynamic SAD curves monotonically decrease alongside the source-target loss gap L, Ly. while fixed-hop or fixed-view vara single fixed hop aggregation does not govern structural discrepancies in multi-view graphs structural discrepancies in multi-view graphs are not governed by any single hop or single relational view; the most transferable view-hop pair evolves during optimization, and capturing this evolution requires a dynamic metric such as SAD.
Moreover, the experiments reveal a consistent performance gap between the two measures. Because relies on a static aggregation operator. its trajectories remain high and fluctuate throughout training. In contrast, SAGA re-estimates SAD at every epoch and updates the dominant view-hop pair (v", K*) accordingly. As shown in Fig. 9, both SAD curves (raw and smoothed) remain substantially lower than the GAA-based curves across all domain-shift settings and exhibit a clear monotonic convergence. This indicates that SAD provides a tighter and more stable estimate of cross-domait structural mismatch and explains why SAGA consistently achieves superior adaptation performance compared to any estimator based on a fixed-hop structural discrepancy.
the GAA discrepancy x=)x)=('<">>>
I VISUALIZATION ANALYSIS ON SAGA DYNAMIC SELECTION
PAP-PN
0.
7XP-W6
255
PWN0
29.0Ap
P0-P12
3.4
PACAP
PMT
7111"
50
130
150
200
40
10
120
150
230
0
40
30
120
100
25
5.0
(a) CN - US
(b) FR - US
(c) DE CN
PAP-P57
PAP-2up
0.0
PAP-PI
5.0
PAP PI
09.09
0.2
PgR-gge
02
123
200
6
40
120
500
200
0.0
40
120
100
205
Spoch
(d) DE EU
(e) A2 + A1
(f) A1 + A2
Figure 10: Each subplot illustrates the evolution of the selected principal view-hop pair throughout training. At early stages, SAGA frequently switches among multiple candidate view combinations as it searches for the most structurally compatible alignment configuration. However, as training progresses and the Structural Aggregation Distance (SAD) stcadily decreases, the selection pattern stabilizes and consistently converges to a same view to same view mapping (e.g., PAP - PAP on ACM, PP > PP on MAG). This behavior reflects the intuitive structural correspondence between source and target graphs and demonstrates that SAGA's dynamic mechanism not only adapts during optimization but also naturally settles into semantically meaningful and stable view alignments.
A core contribution of SAGA lies in its ability to dynamically identify the principal view-order pair that governs cross-domain transferability. Since this mechanism is algorithmically non trivial,
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provide additional visualization analysis to help reviewers intuitively understand how the dynamic selection behaves during training and why it is necessary. the visualizations we provide, each training epoch is associated with two trajectories: the Structural Aggregation Distance SAD, and the selected dominant view-hop pair - The results show clear and consistent pattern across both ACM and MAG datasets. First, SAD decreases steadily as training progresses, indicating that SAGA continuously strengthens structural alignment between the source and target graphs. Unlike fixed hop fixed view variants whose SAD may stagnate or even fluctuate dynamic SAD monotonically declines alongside the reduction during training, reinforcing its role as faithful indicator of structural shift. Second, the dominant view-hop pair naturally evolves over time. Early in training. the model often explores multiple view combinations (e.g.. PAP->PSP or PSP->PAP in ACM; PAP->PP or PP-PAPP in MAG). This reflects the fact that different hops and relational views may temporarily provide more transferable structure during early stage representation shaping. As the embeddings become more aligned, however, the optimal pair gradually stabilizes and importantly, stabilizes to combinations consistent with intuitive graph semanties. For instance, in ACM, the selection converges to PAP->PAP, the view conveying the strongest author paper relational consistency across domains. In MAG, the selection similarly converges to PP-PP, which captures the most domain invariant citation patterns. This convergence behavior confirms two key insights: Dynamic selection necessary, because the best structural signal is not fixed and varies throughout optimization. he final dominant view order is interpretable, consistently settling on the most semantically transferable relation each dataset. Such visual evidence directly validates our design motivation: structural discrepancies in multi-view graphs are governed not by any single fixed hop or view, but by an evolving dominant structural component. SAGA is the first to model this evolution explicitly, and the visualization analysis clearly demonstrates why dynamic selection is essential and how leads to superior adaptation performance.
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Reviews:
[{"review_id": "prbFCUGGNH", "reviewer": "Reviewer_2hby", "summary": "This work proposes a novel research area, i.e., multi-view graph domain adaptation (MGDA), which focuses on learning node embeddings from source multi-view graph and then adapting the learned label information to a target multi-view graph. To address the cross-view, cross-hop, and cross-domain knowledge shift problem, this work introduces Structural Aggregation Distance (SAD) and proposes a framework, SAGA, for MGDA.\n\nThe experiments show SOTA results across two datasets and 12 source-target domain pairs, which demonstrate the power of SAD and the effectiveness of SAGA for MGDA.", "questions": "Please try to address weaknesses.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 1, "presentation": 3, "contribution": 2, "strengths": "1. [Interesting Problem] The proposed multi-view graph adaptation (MGDA) problem is both novel and appealing. This line of research has strong potential for generalization to other domains. The authors also claim to be the first to formally investigate the MGDA problem.\n2. [Well Motivated] The proposed framework is well motivated, grounded in insightful observations about the relationship between SAD and the domain gap. The overall design of SAGA and the research questions are logically coherent and well articulated.\n3. [Attractive Presentation] The paper is clearly organized and well presented. The authors begin with an empirical study that effectively highlights their motivation, followed by a method grounded in optimization loss derived from that study. This logical flow enhances the overall clarity and readability of the paper.", "weaknesses": "However, there are several important issues regarding the **soundness** and presentation details.\n\n1. The empirical study is not convincing.\n   * What is the meaning of $L_s - L_t$. This does not make sense if they denote the equation 14 and equation 15, since Eq. (15) is actually the entropy of predictions, and Eq (14) is cross entropy with labels, and their difference cannot be used to represent domain gap.\n   * Moreover, these plots cannot support the claim \"a smaller Structural Aggregation Distance (SAD) generally indicates small cross-network performance gap between domain\" which is one of the main idea of the whole framework, if the x-axis is the training epoch that is optimized by Eq (16). Under the supervision of Eq. (16) that minimizes SAD and cross entropy simultaneously, it is very natural that the SAD and $L_s-L_t$ will decrease, but this does not mean SAD will *indicate* domain gaps.\n   * Suggestions: in the empirical study, use a more concise loss instead of the complete loss of SAGA. For example, it might be better if the x-axis denotes the minimization of SAD single. Or just show this experiment as an analysis.\n\n2. The problem is not clearly defined and introduced, although the authors claim that problem is one of the important contributions.\n   * The authors introduce MGDA. However, they do not **formally** and clearly define this problem. This may confuse others for the experimental settings and optimization objectives.\n   * The importance of MGDA is not explicitly and intuitively shown. Why MGDA is an important research direction, what specific problem MGDA could address, and how it benefits to down-stream tasks (applications) is not illustrated. Therefore, the **significance** of MGDA is not clear, weakening the overall contributions.\n\n3. Other issues regarding presentations:\n   * Related work is not well organized. It should categorize the literature, and describe their difference and relations to this work.\n   * Notation part is not complete, consistent, and clear. For example, $K_T$ and $K_s$ should be defined in their first use; $G$ and $G^S$ ($G^T$) are defined differently. Why do $V$ and $\\varepsilon$ not involved in $G^S$ ($G^T$)?\n   * Section 4.1 is not complete. Please check Line 282.\n   * Experimental settings are not clear. What are the train set, validation set, and test set?\n   * The current version still has a few typos and format issues.", "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": "r4zpW5a9sq", "reviewer": "Reviewer_rvXw", "summary": "The paper proposes the practical multi-view graph domain adaptation (MGDA) problem and introduces the effective SAD metric and SAGA framework with clear structure and strong motivation. However, it lacks theoretical analysis of SAD (e.g., comparison with MMD), convergence guarantees for dynamic selection, and complexity analysis of SAD computation. Numerous formatting issues are also present.", "questions": "The paper mentions that the computational overhead of SAD is \"practically acceptable\" as the terms do not participate in backpropagation. However, a more detailed complexity analysis of the SAD computation itself is needed for readers to fully assess the scalability of the proposed method. Could the authors please provide a quantitative analysis? \nThe dynamic selection of the principal view-order pair is a central contribution of SAGA. To gain a more intuitive understanding of how this mechanism guides the alignment process during training, a visualization analysis would be highly insightful.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The paper clearly points out the limitation of most existing graph domain adaptation methods, which are largely confined to single-view graphs, and systematically proposes the multi-view graph domain adaptation (MGDA) problem setting, which aligns well with practical application needs. The concept of Structural Aggregation Distance (SAD) is a major highlight of the paper. Through empirical analysis, the effectiveness of dynamically selecting dominant view-order pairs is demonstrated, providing strong motivation for the methodological design. The SAGA framework has a clear structure, incorporating scalable graph encoding, dynamic neighborhood selection, and intra-/inter-domain alignment modules, forming a complete and coherent solution.", "weaknesses": "The paper currently lacks theoretical analysis of the SAD metric. For instance, what are the theoretical connections or advantages of SAD compared to classical distribution discrepancy measures such as MMD or Wasserstein distance? Is there any theoretical guarantee on the convergence of the dynamic selection strategy? Although Section 5.4 mentions that the computational cost is \"practically acceptable\" and Appendix C (Table 5) compares training time and memory usage, there is still a lack of quantitative complexity analysis specifically for the SAD computation itself. Additionally, the paper contains numerous formatting issues, such as missing spaces, improper placement of Figure 3 and Table 2, and punctuation errors (e.g., using commas at the end of sentences).", "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": "Jd6mjddNtN", "reviewer": "Reviewer_HgFv", "summary": "This paper proposes SAGA, a framework for multi-view graph domain adaptation (MGDA). The key idea is to introduce Structural Aggregation Distance (SAD) to quantify cross-domain and cross-hop structural discrepancies and dynamically select dominant view–hop pairs to guide alignment. The method aims to jointly mitigate view-level and hop-level shifts, achieving strong empirical results on ACM and MAG benchmarks.", "questions": "- Can the authors elaborate on how SAD dynamically changes during training? Is it re-estimated per mini-batch, or per epoch?\n\n- Does the model ever backpropagate through SAD values (Eq. 3), or are they detached and used only for view/hop selection?\n\n- Could SAGA be applied when the number of views differs between source and target graphs?\n\n  **Some minor concern**：\n\n- Figures 2–3 captions could explicitly indicate that SAD is *not* a direct loss but an auxiliary metric.\n\n- Typo issue: in line 213 ''viws'' should be ''views''.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- This paper proposes a new Multiview Graph Domain Adaptation (MGDA) setting and introduces a new benchmark that extends the original GDA scenarios to more realistic and diverse environments.\n\n- The paper identifies an important problem domain adaptation across multi-view graphs and provides an intuitive metric (SAD) to quantify multi-hop structural discrepancies.\n\n- The dynamic view–hop selection is a novel perspective that integrates both inter-view and intra-view adaptation.\n\n- The empirical results across multiple benchmarks show promising performance improvements over prior GDA and MGDA baselines.", "weaknesses": "- The definition of SAD is based on a fixed formulation (Eq. 3–4), yet the paper claims to dynamically mitigate this fixed metric during training. It remains unclear how the method achieves mitigation rather than mere re-weighting of a static discrepancy. More discussion is needed to connect the empirical observation that smaller SAD correlates with the mitigation of the performance gap (Fig. 2) to the optimization mechanism that drives SAD reduction. Does the model directly minimize SAD, or is SAD only used to select dominant views? Clarifying this link is crucial to understanding the role of SAD in SAGA.\n\n- While the experiments include many graph-domain adaptation baselines, some common GDA baselines (e.g., [1], [2]) are absent. Including or discussing these would strengthen the empirical validation and confirm SAGA’s advantage.\n\n- SAGA involves multiple loss components ($L_R$, $L_{IA}$, $L_{CA}$, $L_S$, $L_T$) and dynamic SAD computation. How does this computational cost compare to simpler baselines such as HGDA or PA in practice? \n\n- Could you further clarify the relative importance of View and Neighborhood Order in the selection process? Specifically, under what circumstances does the View play a more critical role, and in what cases do different Neighborhood Orders become more influential? \n\n[1] Chen W, Ye G, Wang Y, et al. Smoothness Really Matters: A Simple yet Effective Approach for Unsupervised Graph Domain Adaptation[J]. AAAI, 2025.\n\n[2] Yang L, Chen X, Zhuo J, et al. Disentangled Graph Spectral Domain Adaptation[C]. ICML, 2025.", "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": "Uya3qtXaJx", "reviewer": "Reviewer_AhJc", "summary": "This paper tackles the challenging problem of Multi-view Graph Domain Adaptation (MGDA), where both the source and target domains consist of multiple relational graph views. The authors introduce a novel framework, SAGA (Structural Aggregation Guided Alignment), which dynamically aligns source and target graphs by selecting the most transferable *view–hop* pair based on a newly proposed discrepancy metric, Structural Aggregation Distance (SAD). The SAD quantifies view- and hop-level structural disparities between source and target graphs, and SAGA leverages it to guide dynamic alignment during training. Experimental results on ACM and MAG benchmarks demonstrate notable performance improvements over a variety of GDA baselines.", "questions": "Please see weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper introduces the MGDA setting and explicitly formulates the structural discrepancy problem in multi-view graphs, which extends the conventional GDA formulation and captures richer relational dependencies.\n2. The proposed SAD metric provides an interpretable and quantitative tool to assess cross-view and cross-hop structural differences, showing a clear empirical correlation between smaller SAD values and reduced domain performance gaps.\n3. Extensive experimental validation across ACM (two-view) and MAG (multi-relation) datasets demonstrates consistent and significant performance gains, supported by solid ablation and visualization analyses.\n4. The paper is well-organized and clearly describes how dynamic hop and view selection improve over static designs.", "weaknesses": "1. **Clarity on MGDA vs. GDA definition** \n   While the paper emphasizes MGDA as a more general setting, it would benefit from a clearer formal distinction between standard GDA (single-view) and MGDA (multi-view). Specifically, it remains unclear whether MGDA is simply a multiple relation extension of GDA or a fundamentally different original graph domain adaptation (GDA) paradigm.\n\n2. **Interpretation of SAD and performance correlation** \n   The empirical finding that a smaller SAD corresponds to smaller source–target performance gaps (Fig. 2) is interesting but under-explained. It would strengthen the paper to theoretically justify why minimizing SAD could implies improved transferability, possibly linking SAD to existing domain discrepancy measures (e.g., MMD or spectral distance).\n\n3. **Dataset construction details** \n   Although the paper provides dataset statistics (Table 4), the construction pipeline of multi-view graphs should be more clearly described. For example: How exactly are multiple views (e.g., PAP, PSP, PP) extracted and synchronized between source and target? Are the node sets shared across views, or partially overlapping? How does this multi-view setup differ from standard GDA datasets where only single-view adjacency is considered?  \n\n4. **Comparative coverage and positioning** \n   The relation between SAGA and recent homophily-aware GDA frameworks such as HGDA could be more explicitly discussed. It remains unclear whether SAGA’s view-hop aggregation mechanism could be unified with or compared against homophily–heterophily decomposition strategies.", "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)