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Multi-objective Optimization with Nonnegative Matrix Factorization for Identifying Overlapping Communities in Networks

  • Hongmin Liu
  • Hao LiEmail author
  • Wei Zhao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 682)

Abstract

Community structure is one of the most important properties existing in complex networks, and community detection in complex networks is an intensively investigated problem in recent years. In real-world networks, a node is usually shared by several overlapping communities. The problem of detecting overlapping communities is much more complicated than the hard-partition problem. In this paper, a multi-objective immune algorithm with nonnegative matrix factorization as local search module (MOIA-Net) is proposed to uncover overlapping communities in networks. The proposed algorithm simultaneously optimizes two criteria, negative ratio association and ratio cut, to achieve a preferable soft-partition in networks. It adopts a nonnegative matrix factorization strategy as local search procedure to enhance the search ability. Experiments on synthetic networks show the efficiency of the proposed algorithm.

Keywords

Complex network Overlapping community detection Multi-objective optimization Nonnegative matrix factorization 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  1. 1.School of Computer Science and TechniqueHenan Polytechnic UniversityJiaozuoChina
  2. 2.Key Laboratory of Intelligent Perception and Image UnderstandingXidian UniversityXi’anChina
  3. 3.School of Computer Science and TechnologyXidian UniversityXi’anChina

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