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<p>With the development of the data era, heterogeneous networks have attracted extensive attention. However, community discovery in such networks is often challenged by fuzzy community boundaries caused by structural or semantic sparsity, a problem frequently overlooked in the literature. To this end, this paper proposes FTCDH, a novel algorithm for contrastive community discovery in heterogeneous graphs with fuzzy boundaries. First, the algorithm features an adaptive structural and semantic augmentation module, designed to synergistically enhance key topological and semantic patterns while mitigating the representation ambiguity caused by edge noise or irrelevant information. Second, the algorithm introduces a heterogeneous graph contrastive learning module, which utilizes a triple collaborative constraint mode performing multi-level, same-scale representation comparisons, to guide the model in deeply integrating enhancement information. A contrastive loss function then maximizes the consistency of representations across views, yielding highly discriminative node representations. This approach enhances the model’s adaptability to sparse patterns and its ability to distinguish fuzzy community boundaries. Extensive experiments on heterogeneous networks demonstrate the algorithm’s effectiveness, achieving a improvement of over 14% in NMI compared to the next-best baseline.</p>

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Adaptive triple collaborative learning for contrastive community discovery in heterogeneous graphs with fuzzy boundaries

  • 期刊论文

  • 发表日期：

    2026年3月3日

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  • Weimin Li,
  • Yue Jiang,
  • Mengying Dai,
  • Yan Zhao,
  • Bin Sheng,
  • Quanke Panf,
  • Qun Jin,
  • Can Wang

摘要

With the development of the data era, heterogeneous networks have attracted extensive attention. However, community discovery in such networks is often challenged by fuzzy community boundaries caused by structural or semantic sparsity, a problem frequently overlooked in the literature. To this end, this paper proposes FTCDH, a novel algorithm for contrastive community discovery in heterogeneous graphs with fuzzy boundaries. First, the algorithm features an adaptive structural and semantic augmentation module, designed to synergistically enhance key topological and semantic patterns while mitigating the representation ambiguity caused by edge noise or irrelevant information. Second, the algorithm introduces a heterogeneous graph contrastive learning module, which utilizes a triple collaborative constraint mode performing multi-level, same-scale representation comparisons, to guide the model in deeply integrating enhancement information. A contrastive loss function then maximizes the consistency of representations across views, yielding highly discriminative node representations. This approach enhances the model’s adaptability to sparse patterns and its ability to distinguish fuzzy community boundaries. Extensive experiments on heterogeneous networks demonstrate the algorithm’s effectiveness, achieving a improvement of over 14% in NMI compared to the next-best baseline.