The Multi-Dimensional Information Fusion Community Discovery Based on Topological Potential

Abstract

Many community discovery algorithms add attribute information of nodes to further improve the quality of community division in the complex network with redundant and discrete data, but these algorithms lack of multi-dimensional information, such as users' interests in social networks, social relations, geography and education background, in addition to topological structure and attribute information. Therefore,this paper proposes a Multi-dimensional Information Fusion Community Discovery(MIFCD) method. Firstly, based on the idea of label propagation, link information and attribute information are combined to get link weights between nodes. Secondly, link weights are added to the topology potential to divide the sub group communities. Finally, the sub group communities are combined by using the distance information and attribute information of the core nodes between communities. In order to verify the effectiveness of the algorithm proposed in this paper, the algorithm is compared with six community partition algorithms which only consider the link information of nodes and consider the two kinds of information of node attributes and links. Experiment results on eight social networks show that this method can effectively improve the quality of community classification in both attribute communities and non-attribute communities by analyzing four evaluation indexes: improved modular degree, information entropy, community overlap degree and comprehensive index. INDEX TERMS Community division, complex network, discrete data, multi-dimensional information.

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Overlapping Community Detection Based on Strong Tie Detection and Non-Overlapping Link Clustering

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