Distributed Top-k Pattern Mining

Abstract

Frequent pattern mining (\(\mathsf {FPM}\)) on a single large graph has been receiving increasing attention since it is crucial to applications in a variety of domains including e.g., social network analysis. The \(\mathsf {FPM}\) problem is defined as finding all the subgraphs (a.k.a. patterns) that appear frequently in a large graph according to a user-defined frequency threshold. In recent years, a host of techniques have been developed, while most of them suffers from high computational cost and inconvenient result inspection. To tackle the issues, in this paper, we propose an approach to mining top-k patterns from a single graph G under the distributed scenario. We formalize the distributed top-k pattern mining problem by incorporating viable support and interestingness metrics. We then develop a parallel algorithm, that preserves early termination property, to efficiently discover top-k patterns. Using real-life and synthetic graphs, we experimentally verify that our algorithm is rather effective and outperforms traditional counterparts in both efficiency and scalability.