6th International ICST Conference on Communications and Networking in China

Research Article

Network Soft Partition Based on Topological Potential

  • @INPROCEEDINGS{10.1109/ChinaCom.2011.6158249,
        author={Jianpei Zhang and Hongbo Li and Jing Yang and Jinbo Bai and Yan Chu},
        title={Network Soft Partition Based on Topological Potential},
        proceedings={6th International ICST Conference on Communications and Networking in China},
        publisher={IEEE},
        proceedings_a={CHINACOM},
        year={2012},
        month={3},
        keywords={social network complex network soft partitioning topological potential},
        doi={10.1109/ChinaCom.2011.6158249}
    }
    
  • Jianpei Zhang
    Hongbo Li
    Jing Yang
    Jinbo Bai
    Yan Chu
    Year: 2012
    Network Soft Partition Based on Topological Potential
    CHINACOM
    IEEE
    DOI: 10.1109/ChinaCom.2011.6158249
Jianpei Zhang1, Hongbo Li1,*, Jing Yang1, Jinbo Bai2, Yan Chu1
  • 1: College of Computer Science and Technology, Harbin Engineering University, Harbin, China
  • 2: School of Economics and Management, Harbin Engineering University, Harbin, China; Department of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin, China
*Contact email: islhb@126.com

Abstract

Partitioning of complex networks, esp. of social networks, has been a hotly debated topic in academic circles in recent years. Since actual networks usually contain some boundary nodes that are difficult to assign to a certain community, soft partitioning is under great demand in practical applications. However, at present network partitioning is done mainly by hard partition, soft partition methods are not common. In this context, a soft partition method is proposed hereby based on topological potential and specific algorithms are also provided. This method not only considers the spread of the uncertainty of community-identity of the boundary nodes in the network, but also realizes a quantified representation of the community-identity of the boundary nodes. Experiments show that this method yields results that are consistent with those by classic methods and is more reasonable.