research-article

Graph Domain Adaptation: A Generative View

Authors:

Kun ZhangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 3

Article No.: 60, Pages 1 - 24

https://doi.org/10.1145/3631712

Published: 12 January 2024 Publication History

Abstract

Recent years have witnessed tremendous interest in deep learning on graph-structured data. Due to the high cost of collecting labeled graph-structured data, domain adaptation is important to supervised graph learning tasks with limited samples. However, current graph domain adaptation methods are generally adopted from traditional domain adaptation tasks, and the properties of graph-structured data are not well utilized. For example, the observed social networks on different platforms are controlled not only by the different crowds or communities but also by domain-specific policies and background noise. Based on these properties in graph-structured data, we first assume that the graph-structured data generation process is controlled by three independent types of latent variables, i.e., the semantic latent variables, the domain latent variables, and the random latent variables. Based on this assumption, we propose a disentanglement-based unsupervised domain adaptation method for the graph-structured data, which applies variational graph auto-encoders to recover these latent variables and disentangles them via three supervised learning modules. Extensive experimental results on two real-world datasets in the graph classification task reveal that our method not only significantly outperforms the traditional domain adaptation methods and the disentangled-based domain adaptation methods but also outperforms the state-of-the-art graph domain adaptation algorithms. The code is available at https://github.com/rynewu224/GraphDA.

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Index Terms

Graph Domain Adaptation: A Generative View
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 18, Issue 3

April 2024

663 pages

EISSN:1556-472X

DOI:10.1145/3613567

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Publication History

Published: 12 January 2024

Online AM: 14 November 2023

Accepted: 22 October 2023

Revised: 19 July 2023

Received: 21 September 2022

Published in TKDD Volume 18, Issue 3

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National Key R&D Program of China
National Science Fund for Excellent Young Scholars
Natural Science Foundation of China
Science and Technology Planning Project of Guangzhou
Guangdong Provincial Science and Technology Innovation Strategy Fund

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Cited By

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https://doi.org/10.1016/j.ipm.2024.103944
Li ZLiang SShi Jvan Leeuwen M(2024)Cross-Domain Graph Level Anomaly DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.346244236:12(7839-7850)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3462442
Chen ZJi JChen KNi QDing XYu W(2024)Adaptive Topology-Aware Siamese Network for Cross-Domain Fault Diagnosis With Small SamplesIEEE Sensors Journal10.1109/JSEN.2024.341646024:15(24438-24451)Online publication date: 1-Aug-2024
https://doi.org/10.1109/JSEN.2024.3416460
Zhong RHu BHong ZZhang ZLou SSong XFeng YTan J(2024)Human-Robot handover task intention recognition framework by fusing human digital twin and deep domain adaptationJournal of Engineering Design10.1080/09544828.2024.2326111(1-17)Online publication date: 7-Mar-2024
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Qiao ZXiao MGuo WLuo XXiong H(2024)Information filtering and interpolating for semi-supervised graph domain adaptationPattern Recognition10.1016/j.patcog.2024.110498153(110498)Online publication date: Sep-2024
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Liu MZhang ZMa NGu MWang HZhou SBu J(2024)Structure enhanced prototypical alignment for unsupervised cross-domain node classificationNeural Networks10.1016/j.neunet.2024.106396177(106396)Online publication date: Sep-2024
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