Community Detection in Attributed Graphs: An Embedding Approach


  • Ye Li Fudan University
  • Chaofeng Sha Fudan University
  • Xin Huang Hong Kong Baptist University
  • Yanchun Zhang Fudan University, Victoria University



social networking and community identification


Community detection is a fundamental and widely-studied problem that finds all densely-connected groups of nodes and well separates them from others in graphs. With the proliferation of rich information available for entities in real-world networks, it is useful to discover communities in attributed graphs where nodes tend to have attributes. However, most existing attributed community detection methods directly utilize the original network topology leading to poor results due to ignoring inherent community structures. In this paper, we propose a novel embedding based model to discover communities in attributed graphs. Specifically, based on the observation of densely-connected structures in communities, we develop a novel community structure embedding method to encode inherent community structures via underlying community memberships. Based on node attributes and community structure embedding, we formulate the attributed community detection as a nonnegative matrix factorization optimization problem. Moreover, we carefully design iterative updating rules to make sure of finding a converging solution. Extensive experiments conducted on 19 attributed graph datasets with overlapping and non-overlapping ground-truth communities show that our proposed model CDE can accurately identify attributed communities and significantly outperform 7 state-of-the-art methods.




How to Cite

Li, Y., Sha, C., Huang, X., & Zhang, Y. (2018). Community Detection in Attributed Graphs: An Embedding Approach. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).