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.