MPSI: A Multi-pathogenic Susceptible-infected Algorithm for Overlapping Community Detection in Complex Networks

Jin-yan LI, Yi-ru WEN, Hai-ling XIONG


There is a huge number of complex networks in our life, it is of great significance to study its topological structure and related properties. Community Structure is one of the most common and important property, existing in most networks. Further research found overlapping community is more close to real-world networks. As a result, overlapping community detection is put forward for revealing the intrinsic structure and properties of complex networks. A huge number of algorithms have been proposed to discover community structures. Based on these principles and existing researches, a fast and efficient algorithm for detecting overlapping community structures is proposed in this paper, called Multi-Pathogenic-Susceptible-Infected (MPSI). It improves the efficiency of division, controls the level of overlap, and avoids unnecessary overlap division. Experimental results on four real-world networks demonstrate that the proposed method achieves high accuracy on detecting overlapping community in networks.


Community detection, Overlapping community structure, Infectious disease model


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