A Two-Stage Overlapping Community Detection Based on Structure and Node Attributes in Online Social Networks

  • Xinmeng ZhangEmail author
  • Xinguang Li
  • Shengyi Jiang
  • Xia Li
  • Bolin Xie
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1072)


Traditional community detection algorithms are mainly based on network structure, while ignoring a large number of node attributes. In this paper, we propose a two-stage overlapping community detection method which combines structure and attributes(tsocd-SA). First, a set of non-overlapping communities are identified by using existing community detection methods, and community attribute summaries which represents high degree homogeneous attribute value of a community are constructed according to the attributes of the special nodes in the community. Then, we propose a similarity measure between node and community based on network structure and community attribute summary. For connector nodes which connect more than one communities, each node is divided into one or more communities based on the similarity and a specific threshold r. Experimental results in online social network datasets show that our proposed method is more effective than solely focus on structural information.


Overlapping community detection Online social network Community attributes summary Similarity 



This research is supported by the National Natural Science Foundation of China (No. 62877013, 61402119).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Xinmeng Zhang
    • 1
    • 2
    • 3
    Email author
  • Xinguang Li
    • 1
  • Shengyi Jiang
    • 2
    • 3
  • Xia Li
    • 2
    • 3
  • Bolin Xie
    • 2
    • 3
  1. 1.Laboratory of Language Engineering and ComputingGuangdong University of Foreign StudiesGuangzhouChina
  2. 2.Non-universal Language Intelligent Processing LaboratoryGuangdong University of Foreign StudiesGuangzhouChina
  3. 3.School of Information Science and TechnologyGuangdong University of Foreign StudiesGuangzhouChina

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