Evolution of regular directed patterns in dynamic social networks

Existing Dynamic Graph mining algorithms focus typically on finding patterns in undirected, unweighted and weighted dynamic networks ignoring the fact that some of them could be directed also. In this paper, we focus on finding regular evolution patterns in edges, outdegree and indegree of all the nodes, featuring consecutively at fixed time intervals during the growth of an unweighted and directed dynamic graph. Such regular patterns will help in finding the characteristics (such as popularity, inactiveness) of the nodes. A methodology of occurrence rule is proposed in order to determine regular evolution patterns, which are considered to be regular if they follow the same occurrence rule. The methodology is also used to find the patterns in the outdegree and indegree of all the nodes. These patterns are used to describe exhaustively the neighbourhood properties of dynamic graphs as in the social networks. To ensure its practical feasibility, the method has been applied to real world dataset on Facebook-like Forum network, and the results have shown that 37.6% of the edges are directed regular edges, and 59% of the total users, not having their indegree patterns are unpopular users.