Community detection based on links and node features in social networks

Fengli Zhang, Jun Li, Feng Li, Min Xu, Richard Xu, Xiangjian He

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)


Community detection is a significant but challenging task in the field of social network analysis. Many effective methods have been proposed to solve this problem. However, most of them are mainly based on the topological structure or node attributes. In this paper, based on SPAEM [1], we propose a joint probabilistic model to detect community which combines node attributes and topological structure. In our model, we create a novel feature-based weighted network, within which each edge weight is represented by the node feature similarity between two nodes at the end of the edge. Then we fuse the original network and the created network with a parameter and employ expectation-maximization algorithm (EM) to identify a community. Experiments on a diverse set of data, collected from Facebook and Twitter, demonstrate that our algorithm has achieved promising results compared with other algorithms.

Original languageEnglish
Title of host publicationMultiMedia Modeling - 21st International Conference, MMM 2015, Proceedings
EditorsXiangjian He, Dacheng Tao, Muhammad Abul Hasan, Suhuai Luo, Changsheng Xu, Jie Yang
PublisherSpringer Verlag
Number of pages12
ISBN (Electronic)9783319144443
Publication statusPublished - 2015
Externally publishedYes
Event21st International Conference on MultiMedia Modeling, MMM 2015 - Sydney, Australia
Duration: 5 Jan 20157 Jan 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st International Conference on MultiMedia Modeling, MMM 2015


  • Community Detection
  • EM algorithm
  • Node Similarity
  • Social Network

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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