Unsupervised domain adaptive graph convolutional networks

Man Wu, Shirui Pan, Chuan Zhou, Xiaojun Chang, Xingquan Zhu

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

30 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of The World Wide Web Conference WWW 2020
EditorsTie-Yan Liu, Maarten van Steen
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages11
ISBN (Electronic)9781450370233
Publication statusPublished - Apr 2020
EventInternational World Wide Web Conference 2020 - Taipei, Taiwan
Duration: 20 Apr 202024 Apr 2020
Conference number: 29th
https://dl.acm.org/doi/proceedings/10.1145/3366423 (Proceedings)
https://www2020.thewebconf.org (Website)


ConferenceInternational World Wide Web Conference 2020
Abbreviated titleWWW 2020
Internet address


  • Domain Adaptation
  • graph convolutional networks
  • node classification

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