GCLS$^2$: Towards Efficient Community Detection Using Graph Contrastive Learning with Structure Semantics Article Swipe
Wen Qi
,
Yiyang Zhang
,
Yutong Ye
,
Yingbo Zhou
,
Nan Zhang
,
Xiang Lian
,
Mingsong Chen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.11273
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.11273
Due to the power of learning representations from unlabeled graphs, graph contrastive learning (GCL) has shown excellent performance in community detection tasks. Existing GCL-based methods on the community detection usually focused on learning attribute representations of individual nodes, which, however, ignores structural semantics of communities (e.g., nodes in the same community should be structurally cohesive). Therefore, in this paper, we will consider the community detection under the community structure semantics and propose an effective framework for graph contrastive learning under structure semantics (GCLS$^2$) to detect communities. To seamlessly integrate interior dense and exterior sparse characteristics of communities with our contrastive learning strategy, we employ classic community structures to extract high-level structural views and design a structure semantic expression module to augment the original structural feature representation. Moreover, we formulate the structure contrastive loss to optimize the feature representation of nodes, which can better capture the topology of communities. To adapt to large-scale networks, we design a high-level graph partitioning (HGP) algorithm that minimizes the community detection loss for GCLS$^2$ online training. It is worth noting that we prove a lower bound on the training of GCLS$^2$ from the perspective of the information theory, explaining why GCLS$^2$ can learn a more accurate representation of the structure. Extensive experiments have been conducted on various real-world graph datasets and confirmed that GCLS$^2$ outperforms nine state-of-the-art methods, in terms of the accuracy, modularity, and efficiency of detecting communities.
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- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.11273
- https://arxiv.org/pdf/2410.11273
- OA Status
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- Related Works
- 10
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https://openalex.org/W4403573624Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2410.11273Digital Object Identifier
- Title
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GCLS$^2$: Towards Efficient Community Detection Using Graph Contrastive Learning with Structure SemanticsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-15Full publication date if available
- Authors
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Wen Qi, Yiyang Zhang, Yutong Ye, Yingbo Zhou, Nan Zhang, Xiang Lian, Mingsong ChenList of authors in order
- Landing page
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https://arxiv.org/abs/2410.11273Publisher landing page
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https://arxiv.org/pdf/2410.11273Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2410.11273Direct OA link when available
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Computer science, Graph, Artificial intelligence, Natural language processing, Semantics (computer science), Theoretical computer science, Programming languageTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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