Inspired by the principle of homophily, most existing graph clustering approaches assume that the formation of clusters is highly related to node attributes, and thus leverage node information to improve graph clustering performance. However, utilizing all attributes as supplemental information for graph clustering may fail on real-world attributed graphs since only a subset of attributes are truly relevant for the formation of clusters, and the relevant attributes (i.e., attribute subspaces) for different clusters often differ largely in real-world graphs. Therefore, in this paper, we propose a subspace stochastic block model (SSB) to explore the cluster structures in attributed graphs. The key point is to view both topological structure and attribute information as the latent factors to drive the formation of clusters in the new proposed generative model. More specifically, relevant attributes are iteratively learned for each cluster, and subsequently used as valuable information to be integrated into the stochastic block model. To solve the likelihood function, an expectation–maximization strategy is developed to infer all parameters efficiently, and finally all clusters and their corresponding attribute subspaces are identified simultaneously. Extensive experimental results on both synthetic and real-world graphs have demonstrated the effectiveness of SSB, and show its superiority over many state-of-art approaches.

受同构原理的启发，大多数现有的图聚类方法都假设聚类的形成与节点属性高度相关，因此可以利用节点信息来提高图聚类性能。但是，将所有属性用作图聚类的补充信息在现实世界中的属性图上可能会失败，因为只有属性的一个子集与聚类的形成真正相关，并且不同聚类的相关属性（即属性子空间）通常存在很大差异在现实世界的图表中。因此，在本文中，我们提出了一个子空间随机块模型（SSB）来探索属性图中的聚类结构。关键是将拓扑结构和属性信息视为驱动新提出的生成模型中簇形成的潜在因素。更具体地说，为每个集群迭代学习相关属性，然后将其用作有价值的信息以集成到随机块模型中。为了解决似然函数，开发了一种期望最大化策略来有效地推断所有参数，最后同时识别所有聚类及其对应的属性子空间。在合成图和实际图上的大量实验结果证明了SSB的有效性，并显示了它比许多最新方法的优越性。并随后用作有价值的信息，以集成到随机块模型中。为了解决似然函数，开发了期望最大化策略来有效地推断所有参数，最后同时识别所有聚类及其对应的属性子空间。在合成图和实际图上的大量实验结果证明了SSB的有效性，并显示了它比许多最新方法的优越性。并随后用作有价值的信息，以集成到随机块模型中。为了解决似然函数，开发了一种期望最大化策略来有效地推断所有参数，最后同时识别所有聚类及其对应的属性子空间。在合成图和实际图上的大量实验结果证明了SSB的有效性，并显示了它比许多最新方法的优越性。