Statistical approach for community mining in social networks

Abstract

The popularity of social networking on the Web and the explosive combination with data mining techniques open up vast and so far unexplored opportunities for social intelligence on the Web. A network community is a special sub-network that contains a group of nodes sharing similar linked patterns. Many community mining algorithms have been developed in the past. In this work, we have presented a new algorithm BFC (breadth first clustering) which uses statistical approach for community mining in social networks. The algorithm proceeds in breadth first way and incrementally extract communities from the network. This algorithm is simple, fast and can be scaled easily for large social networks. The effectiveness of this approach has been validated using network examples.