Mining regular pattern in edge labeled dynamic graph

Static Graphs consist of a fixed sequence of nodes and edges which does not change over time, hence lack in providing the information regarding evolution of the network. In contrast, Dynamic Graphs to a greater extent relate to real-life events and so provide complete information about the network evolution. That is why many researchers [1, 2, 3, 5, 6, 7, 8, 9 and 10] have developed interest in mining of Dynamic Graphs. We feel, that the topic can be further sub-divided structurally into four major categories, which are mining of Labeled, Edge Unlabeled, Directed and Undirected Dynamic Graphs. However, the main focus of research till now is on the mining of Edge Unlabeled Dynamic Graphs. But the limitation is that it does not provide the complete insights of graphs where edge strengths i.e. weights are also changing with time. For example in case of Coauthor network mining in Unlabeled Dynamic Graphs gives information only about the occurrence of relation whereas that in Labeled Dynamic Graphs provides more detailed information like the number of paper published jointly at different instants of time. To address this problem, the present paper proposes a novel method to find out Weighted Regular Patterns in Edge Labeled Dynamic Graphs. The proposed method consists of creating a summary graph to find weight occurrence sequence of edges enabling to determine weighted regular patterns. The method is applied to real world dataset, PACS networks, to ensure its practical feasibility and to understand how Weighted Dynamic Graphs behave regularly over time.