research-article

Adversarial Separation Network for Cross-Network Node Classification

Authors:

Chongjun WangAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 2618 - 2626

https://doi.org/10.1145/3459637.3482228

Published: 30 October 2021 Publication History

Abstract

Node classification is an important yet challenging task in various network applications, and many effective methods have been developed for a single network. While for cross-network scenarios, neither single network embedding nor traditional domain adaptation can directly solve the task. Existing approaches have been proposed to combine network embedding and domain adaptation for cross-network node classification. However, they only focus on domain-invariant features, ignoring the individual features of each network, and they only utilize 1-hop neighborhood information (local consistency), ignoring the global consistency information. To tackle the above problems, in this paper, we propose a novel model, Adversarial Separation Network(ASN), to learn effective node representations between source and target networks. We explicitly separate domain-private and domain-shared information. Two domain-private encoders are employed to extract the domain-specific features in each network and a shared encoder is employed to extract the domain-invariant shared features across networks. Moreover, in each encoder, we combine local and global consistency to capture network topology information more comprehensively. ASN integrates deep network embedding with adversarial domain adaptation to reduce the distribution discrepancy across domains. Extensive experiments on real-world datasets show that our proposed model achieves state-of-the-art performance in cross-network node classification tasks compared with existing algorithms.

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Cited By

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
Yang HHe HZhang WWang YJing L(2024)Multi-Source and Multi-modal Deep Network Embedding for Cross-Network Node ClassificationACM Transactions on Knowledge Discovery from Data10.1145/365330418:6(1-26)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3653304
Shen XShao MPan SYang LZhou X(2024)Domain-Adaptive Graph Attention-Supervised Network for Cross-Network Edge ClassificationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.330963235:12(17842-17855)Online publication date: Dec-2024
https://doi.org/10.1109/TNNLS.2023.3309632
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Index Terms

Adversarial Separation Network for Cross-Network Node Classification
1. Computing methodologies
  1. Machine learning
2. Information systems

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

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Published: 30 October 2021

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the Key Research and Development Program of Jiangsu
the National Key Research and Development Program of China
the National Natural Science Foundation of China

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CIKM '21

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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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
Yang HHe HZhang WWang YJing L(2024)Multi-Source and Multi-modal Deep Network Embedding for Cross-Network Node ClassificationACM Transactions on Knowledge Discovery from Data10.1145/365330418:6(1-26)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3653304
Shen XShao MPan SYang LZhou X(2024)Domain-Adaptive Graph Attention-Supervised Network for Cross-Network Edge ClassificationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.330963235:12(17842-17855)Online publication date: Dec-2024
https://doi.org/10.1109/TNNLS.2023.3309632
Zhang YShi CLi XZhang ZHu X(2024)Multicomponent Similarity Graphs for Cross-Network Node ClassificationIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.33071055:3(1411-1424)Online publication date: Mar-2024
https://doi.org/10.1109/TAI.2023.3307105
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
https://doi.org/10.1016/j.patcog.2024.110498
Wu HTian LWu YZhang JNg MLong J(2024)Transferable graph auto-encoders for cross-network node classificationPattern Recognition10.1016/j.patcog.2024.110334150:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.patcog.2024.110334
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
https://doi.org/10.1016/j.neunet.2024.106396
Xiao JDai QShen XXie XDai JLam JKwok K(2024)Semi-supervised domain adaptation on graphs with contrastive learning and minimax entropyNeurocomputing10.1016/j.neucom.2024.127469580:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127469
Wu HWu YLi NYang MZhang JNg MLong J(2024)High-order proximity and relation analysis for cross-network heterogeneous node classificationMachine Learning10.1007/s10994-024-06566-3113:9(6247-6272)Online publication date: 19-Jun-2024
https://doi.org/10.1007/s10994-024-06566-3
Ding KLiu YZhang CWang J(2024)Data‐efficient graph learning: Problems, progress, and prospectsAI Magazine10.1002/aaai.12200Online publication date: 18-Oct-2024
https://doi.org/10.1002/aaai.12200
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