Statistical Outlook for Community Mining in Social Networks

Social networking is very popular on the web and the combination with data mining techniques open up more opportunities for social intelligence on. A Social Network can be viewed as a complex interconnection of social entities, comprises of social structure of nodes tied together with one or more type of relationship namely share interests, activities, friendship, background, dislike, trade, financial exchange, etc. Mining a social is the work of grouping these social entities and there patterns for further discrimination, characterization, classification. Much research has been done in the past on social mining algorithm. In this work, we will present a new algorithm Breadth First Droving (BFD) which uses statistical outlook for social mining in Social networks. The algorithm proceeds in breadth first way and incrementally extract communities from the Network. This algorithm can be scaled easily for large Social networks and also fast and simple.