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<p>Community detection is a key task for revealing functional organization in complex networks. Graph neural networks (GNNs) capture non-linear relationships but often suffer from over-smoothing as layers increase. Deep nonnegative matrix factorization (DNMF) models are interpretable via hierarchical learning but are linear and sensitive to topological noise. We propose GCNCDNMF, a framework that integrates graph convolutional networks (GCNs) with a constrained deep nonnegative matrix factorization (CDNMF) module. The CDNMF component uses a deep autoencoder-like structure with graph regularization to preserve community structures. The hierarchical embeddings from CDNMF are injected into the GCN propagation steps, which mitigates over-smoothing. In return, the GCN’s non-linear reconstructions refine the network topology and feed back to CDNMF, improving noise robustness. Experiments on five real-world benchmark datasets (Cora, Citeseer, Email, Cornell, and Texas) show that GCNCDNMF consistently outperforms state-of-the-art methods in accuracy, normalized mutual information, and adjusted Rand index. The code for this work is publicly available at: <a href="https://github.com/LiShunli0719/GCNCDNMF_main">https://github.com/LiShunli0719/GCNCDNMF_main</a>.</p>

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Constrained deep nonnegative matrix factorization with graph convolutional networks for unsupervised community detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月7日

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  • Shunli Li,
  • Ling Wang,
  • Mingjun Bai

摘要

Community detection is a key task for revealing functional organization in complex networks. Graph neural networks (GNNs) capture non-linear relationships but often suffer from over-smoothing as layers increase. Deep nonnegative matrix factorization (DNMF) models are interpretable via hierarchical learning but are linear and sensitive to topological noise. We propose GCNCDNMF, a framework that integrates graph convolutional networks (GCNs) with a constrained deep nonnegative matrix factorization (CDNMF) module. The CDNMF component uses a deep autoencoder-like structure with graph regularization to preserve community structures. The hierarchical embeddings from CDNMF are injected into the GCN propagation steps, which mitigates over-smoothing. In return, the GCN’s non-linear reconstructions refine the network topology and feed back to CDNMF, improving noise robustness. Experiments on five real-world benchmark datasets (Cora, Citeseer, Email, Cornell, and Texas) show that GCNCDNMF consistently outperforms state-of-the-art methods in accuracy, normalized mutual information, and adjusted Rand index. The code for this work is publicly available at: https://github.com/LiShunli0719/GCNCDNMF_main.