Multidimensional Community Discovering In Heterogeneous Social Networks

Multidimensional community discovering in heterogeneous social networks is an important issue. Many approaches have been proposed for community discovering in heterogeneous networks. However, they have focused only on topological properties of these networks, ignoring the embedded semantic information. As the solution to this information glut limit, we propose, in this article, a new multidimensional community discovering approach, which incorporates the multiple types of objects and relationships, derived from a heterogeneous networks. First, we propose to construct the concept lattice family CLF to represent the different objects and relations of the heterogeneous social networks based on the relational concept analysis techniques. Then after we introduce a new algorithm that explores such CLF and extract the multidimensional communities. Carried out experiments on real datasets enhance the effectiveness of our proposal and open promising issues.