Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection, because of its better interpretability. However, the existing NMF-based methods have the following three problems: 1) they directly transform the original network into community membership space, so it is difficult for them to capture the hierarchical information; 2) they often only pay attention to the topology of the network and ignore its node attributes; 3) it is hard for them to learn the global structure information necessary for community detection. Therefore, we propose a new community detection algorithm, named Contrastive Deep Nonnegative Matrix Factorization (CDNMF). Firstly, we deepen NMF to strengthen its capacity for information extraction. Subsequently, inspired by contrastive learning, our algorithm creatively constructs network topology and node attributes as two contrasting views. Furthermore, we utilize a debiased negative sampling layer and learn node similarity at the community level, thereby enhancing the suitability of our model for community detection. We conduct experiments on three public real graph datasets and the proposed model has achieved better results than state-of-the-art methods. Code available at https://github.com/6lyc/CDNMF.git.

翻译：近年来，非负矩阵分解因其良好的可解释性而被广泛应用于社区检测。然而，现有基于非负矩阵分解的方法存在以下三个问题：1）它们直接将原始网络映射到社区隶属空间，因此难以捕捉层次化信息；2）它们通常仅关注网络的拓扑结构而忽略节点属性；3）它们难以学习社区检测所需的全局结构信息。为此，我们提出一种新的社区检测算法——对比深度非负矩阵分解。首先，我们深化非负矩阵分解以增强其信息提取能力。其次，受对比学习启发，该算法创新性地将网络拓扑结构与节点属性构建为两个对比视图。此外，我们采用去偏负采样层，并在社区层面学习节点相似度，从而提升模型对社区检测任务的适用性。我们在三个公开真实图数据集上进行了实验，所提模型取得了优于现有最优方法的结果。代码地址：https://github.com/6lyc/CDNMF.git。