Association Rules with Graph Patterns

We propose graph-pattern association rules (GPARs) for social media marketing. Extending association rules for itemsets, GPARs help us discover regularities between entities in social graphs, and identify potential customers by exploring social influence. We study the problem of discovering topk diversified GPARs. While this problem is NP-hard, we develop a parallel algorithm with accuracy bound. We also study the problem of identifying potential customers with GPARs. While it is also NP-hard, we provide a parallel scalable algorithm that guarantees a polynomial speedup over sequential algorithms with the increase of processors. Using real-life and synthetic graphs, we experimentally verify the scalability and effectiveness of the algorithms.