Capturing More Associations by Referencing External Graphs

This paper studies association rule discovery in a graph G(1) by referencing an external graph G(2) with overlapping information. The objective is to enrich G(1) with relevant properties and links from G(2). As a testbed, we consider Graph Association Rules (GARs). We propose a notion of graph joins to enrich G(1) by aligning entities across G(1) and G(2). We also introduce a graph filtering method to support graph joins, by fetching only the data of G(2) that pertains to the entities of G(1), to reduce noise and the size of the fused data. Based on these we develop a parallel algorithm to discover GARs across G(1) and G(2). Moreover, we provide an incremental GAR discovery algorithm in response to updates to G(1) and G(2). We show that both algorithms guarantee to reduce parallel runtime when given more processors. Better yet, the incremental algorithm is bounded relative to the batch one. Using real-life and synthetic data, we empirically verify that the methods improve the accuracy of association analyses by 30.4% on average, and scale well with large graphs.