Community Mining in Signed Social Networks -An Automated Approach

A social network can be viewed as a complex interconnection of social entities. Mining a community is the task of grouping these social entities together on the basis of their linked pattern. A lot of research has been done on this subject but most of them were only concerned with the unsigned graph. Our work is primarily for the networks having both positive and negative relations; these networks are known as signed social network. In this work, we propose CRA (Clustering re-clustering algorithm) which works in two phases. The first phase is based on Breadth First Search algorithm which forms clusters on the basis of the positive links only. The second phase takes the output of first phase as its input and produces clusters on the basis of a robust criteria termed as participation level. Our algorithm can mine the signed social networks where the negative inter-community links and the positive intra-community links are dense. The algorithm is also useful in mining the communities from positive only conventional graphs. Moreover it doesn't require any external parameter for its operation as is the case with other algorithms like FEC. Inclusion of a new node in the graph is tackled effectively to reduce the unnecessary computation.