research-article

Unsupervised Domain Adaptive Graph Convolutional Networks

Authors:

Xingquan ZhuAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 1457 - 1467

https://doi.org/10.1145/3366423.3380219

Published: 20 April 2020 Publication History

Abstract

Graph convolutional networks (GCNs) have achieved impressive success in many graph related analytics tasks. However, most GCNs only work in a single domain (graph) incapable of transferring knowledge from/to other domains (graphs), due to the challenges in both graph representation learning and domain adaptation over graph structures. In this paper, we present a novel approach, unsupervised domain adaptive graph convolutional networks (UDA-GCN), for domain adaptation learning for graphs. To enable effective graph representation learning, we first develop a dual graph convolutional network component, which jointly exploits local and global consistency for feature aggregation. An attention mechanism is further used to produce a unified representation for each node in different graphs. To facilitate knowledge transfer between graphs, we propose a domain adaptive learning module to optimize three different loss functions, namely source classifier loss, domain classifier loss, and target classifier loss as a whole, thus our model can differentiate class labels in the source domain, samples from different domains, the class labels from the target domain, respectively. Experimental results on real-world datasets in the node classification task validate the performance of our method, compared to state-of-the-art graph neural network algorithms.

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Index Terms

Unsupervised Domain Adaptive Graph Convolutional Networks
1. Information systems
2. Theory of computation

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Published In

cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

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Published: 20 April 2020

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April 20 - 24, 2020

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Cited By

Ji SLi WZhang BJi WWang YNg S(2024)Multivariate Prediction Soft Sensor Model for Truck Cranes Based on Graph Convolutional Network and Random ForestActuators10.3390/act1309035713:9(357)Online publication date: 12-Sep-2024
https://doi.org/10.3390/act13090357
Gu JYi YLi Q(2024)Motion sensitive network for action recognition in control and decision-making of autonomous systemsFrontiers in Neuroscience10.3389/fnins.2024.137002418Online publication date: 25-Mar-2024
https://doi.org/10.3389/fnins.2024.1370024
Dan JLiu WLiu MXie CDong SMa GTan YXing JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)HOGDA: Boosting Semi-supervised Graph Domain Adaptation via High-Order Structure-Guided Adaptive Feature AlignmentProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680765(11109-11118)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680765
Zhao TLuo DZhang XWang SBaeza-Yates RBonchi F(2024)Multi-source Unsupervised Domain Adaptation on Graphs with Transferability ModelingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671829(4479-4489)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671829
Mao HDu LZheng YFu QLi ZChen XHan SZhang DAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Source Free Graph Unsupervised Domain AdaptationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635802(520-528)Online publication date: 4-Mar-2024
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Yan YZhang PFang ZLong QChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral PerspectiveProceedings of the ACM Web Conference 202410.1145/3589334.3645620(4328-4339)Online publication date: 13-May-2024
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Zhang ZLiu MWang AChen HLi ZBu JHe BChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional AdaptationProceedings of the ACM Web Conference 202410.1145/3589334.3645507(664-675)Online publication date: 13-May-2024
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Zhu YWang YShi HZhang ZJiao DTang SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer LearningProceedings of the ACM Web Conference 202410.1145/3589334.3645439(539-550)Online publication date: 13-May-2024
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Luo JGu YLuo XJu WXiao ZZhao YYuan JZhang M(2024)GALA: Graph Diffusion-Based Alignment With Jigsaw for Source-Free Domain AdaptationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.341637246:12(9038-9051)Online publication date: Dec-2024
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Gan JHu RMo YKang ZPeng LZhu YZhu X(2024)Multigraph Fusion for Dynamic Graph Convolutional NetworkIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.317258835:1(196-207)Online publication date: Jan-2024
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