Mining in the Proximity of Subgraphs

Graphs are a natural way to represent multi-relational data and are extensively used to model a variety of application domains in diverse fields ranging from bioinformatics to homeland security. Often, in such graphs, certain subgraphs are known to possess some distinct properties and graph patterns in the proximity of these subgraphs can be an indicator of these properties. In this work we focus on the task of mining in the proximity of subgraphs, known to possess certain distinct properties and identify patterns which distinguish these subgraphs from other subgraphs without these properties. This task is novel and of considerable interest as it can facilitate the prediction of previously unknown subgraphs possessing the properties under consideration in the graph and can lead to a better understanding of the application domain. We characterize the task of mining in the proximity of subgraphs as a supervised learning problem and present a heuristic algorithm for the same. Experimental comparison with the ILP system CProgol on real world and artificial datasets provides a strong indication of the ability and viability of the approach in uncovering interesting patterns.