Community Detection from Signed Social Networks Using a Multi-objective Evolutionary Algorithm

  • Yujie Zeng
  • Jing Liu
Conference paper
Part of the Proceedings in Adaptation, Learning and Optimization book series (PALO, volume 1)


In this paper, we propose a method for detecting communities from signed social networks with both positive and negative weights by modeling the problem as a multi-objective problem. In the experiments, both real world and synthetic signed networks whose size ranges from 100 to 1200 nodes are used to validate the performance of the new algorithm. A comparison is also made between the new algorithm and an effective existing algorithm, namely FEC. The experimental results show that our algorithm obtains a good performance on both real world and synthetic data, and outperforms FEC clearly.


Signed social networks Community detection problems Multi-objective evolutionary algorithms 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yujie Zeng
    • 1
  • Jing Liu
    • 1
  1. 1.Key Laboratory of Intelligent Perception and Image Understanding of Ministry of EducationXidian UniversityXi’anChina

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