Community Detection in Multi-dimensional Network

Abstract

Community structure is one of the hidden characteristics of complex network topology. Detecting the community structure is crucial to understand and utilize the network structure. Existing methods are mostly detecting the community on a one-dimensional network, which is based on only one kind of relationship between the nodes. In this paper, as the prerequisite that there are various ways of people connection in real world, we modeling the multiple relationships of group members in the complex network as a multi-dimensional network. Then we used the traditional community detection to infer the community structure from each dimension of our model. Finally, we define the sibling matrix to map the inferred different community structures from the multi-dimensional network into a comprehensive association, so we can find the hidden community of the complex network. Experimental results show that compared with existing algorithms, the proposed algorithm has higher performance.