Percolator: Scalable Pattern Discovery in Dynamic Graphs

Sutanay Choudhury, Sumit Purohit, Peng Lin, Yinghui Wu, Lawrence B. Holder, Khushbu Agarwal

Abstract: We demonstrate \perco, a distributed system for graph pattern discovery in dynamic graphs. In contrast to conventional mining systems, Percolator advocates efficient pattern mining schemes that (1) support pattern detection with keywords; (2) integrate incremental and parallel pattern mining; and (3) support analytical queries such as trend analysis. The core idea of \perco is to dynamically decide and verify a small fraction of patterns and their instances that must be inspected in response to buffered updates in dynamic graphs, with a total mining cost independent of graph size. We demonstrate a( the feasibility of incremental pattern mining by walking through each component of \perco, b) the efficiency and scalability of \perco over the sheer size of real-world dynamic graphs, and c) how the user-friendly \gui of \perco interacts with users to support keyword-based queries that detect, browse and inspect trending patterns. We demonstrate how \perco effectively supports event and trend analysis in social media streams and research publication, respectively.