Entropy-based link selection strategy for multidimensional complex networks

Abstract

Setting up a multidimensional network is an important problem in complex networks and has become a future development trend in the fields of biological gene networks, social networks and so on. A multidimensional network comprises connections and attributes. Community detection in heterogeneous dat asets in different dimensions is more difficult than that in a single network. Traditional methods for dealing with multidimensional networks are ineffective, because of using supervised information or applying strategies for adjusting the graph structure of a single network. In this paper, we propose a semi-supervised community detection method for multidimensional heterogeneous networks. First, we generate a single network by integrating the multidimensional heterogeneous networks. The robust semi-supervised link adjustment strategy is then iteratively applied to the single network to make full use of dynamic supervised information for adding or removing links based on node entropy. Experimental results are obtained by five real multidimensional social datasets. The results show that the proposed method can effectively integrate heterogeneous data. The average accuracy rate and standard mutual information were 90.50% and 93.99%, respectively, representing improvements of 28.97% and 35.06%, respectively, over existing methods.