APPROXIMATING RELATION EXTRACTION FOR COMMUNITY MINING IN HETEROGENEOUS SOCIAL NETWORKS.

Community mining in heterogeneous social networks is different from traditional community mining approaches in that it assumes the existence of more than one kind of relations between objects. It consists of two steps. Relation extraction in which the goal is to combine existing relations to form a relation based on the query provided by the user; and community mining which identifies community structure in the extracted relation. In this paper we are mainly concerned with the first step. Previously proposed algorithms for relation extraction are either not applicable to large data sets or can handle simple queries only. In this paper, we propose two genetic algorithms which use different kind of operators to search the problem space. These algorithms work in general situations, and are more scalable, interpretable, and extensible than previous methods. Experiments and analysis of the results on DBLP dataset show the power of our algorithms in handling complex queries in large data sets. [ABSTRACT FROM AUTHOR]