A Multi-layer Network Community Detection Method via Network Feature Augmentation and Contrastive Learning

Min Teng, Chao Gao, Zhen Wang, Tanimoto Jun

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

Detecting the community structures of multi-layer networks is important for exploring the node functions and revealing the potential network structures. However, the existing methods mainly rely on the intra-layer features and manual labels, which leads to the high computational overhead and cannot ensure the robustness and accuracy in networks with complex community structures. To solve the above problems, this paper proposes a network feature-augmentation contrastive constraint method (named as NFACC), which achieves the high accuracy and robustness by contrasting the feature-augmented and original multi-layer networks. Specifically, NFACC consists of two main models, i.e., a feature-augmented network generation model and a contrastive learning-based node representation model. Firstly, NFACC integrates the intra-layer and inter-layer features of multi-layer networks to form an optimizable feature-augmented network based on the generation model. Then, it obtains the low-dimensional representations of both the augmented network and each layer of the multi-layer network based on the node representation model. By training these two models, NFACC further merges the intra-layer and inter-layer features and improves the robustness against complex network structures. Finally, NFACC achieves accurate community detection through the trained node representations. Extensive experiments demonstrate that the proposed NFACC method outperforms the state-of-the-art methods in detecting the community structure of multi-layer networks.

源语言英语
主期刊名PRICAI 2024
主期刊副标题Trends in Artificial Intelligence - 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, Proceedings
编辑Rafik Hadfi, Takayuki Ito, Patricia Anthony, Alok Sharma, Quan Bai
出版商Springer Science and Business Media Deutschland GmbH
158-169
页数12
ISBN(印刷版)9789819601158
DOI
出版状态已出版 - 2025
活动21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024 - Kyoto, 日本
期限: 18 11月 202424 11月 2024

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
15281 LNAI
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024
国家/地区日本
Kyoto
时期18/11/2424/11/24

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