A Versatile Algorithm for Predictive Graph Rule Mining
Karel Vaculík · ITAT · 2015
Pattern mining in dynamic graphs has received a lot of attention in recent years. However, proposed methods are typically limited to specific classes of patterns expressing only a specific types of changes. In this paper, we propose a new algorithm, DGRMiner, which is able to mine patterns in the form of graph rules capturing various types of changes, i.e. addition and deletion of vertices and edges, and relabeling of vertices and edges. This algorithm works both with directed and undirected dynamic graphs with multiedges. It is designed both for the single-dynamic-graph and the set-of-dynamic-graphs scenarios. The performance of the algorithm has been evaluated by using two real-world and two synthetic datasets.