Percolator: Scalable Pattern Discovery in Dynamic Graphs

Conference · Tue Feb 06 00:00:00 EST 2018

DOI:https://doi.org/10.1145/3159652.3160589· OSTI ID:1433776

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

We demonstrate Percolator, 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 Percolator is to dynamically decide and verify a small fraction of patterns and their in- stances 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 Percolator, b) the efficiency and scalability of Percolator over the sheer size of real-world dynamic graphs, and c) how the user-friendly GUI of Percolator inter- acts with users to support keyword-based queries that detect, browse and inspect trending patterns. We also demonstrate two user cases of Percolator, in social media trend analysis and academic collaboration analysis, respectively.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1433776
• Report Number(s): PNNL-SA-128916; 453040300
• Resource Relation: Conference: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (WSDM 2018), February 5-9, 2018, Los Angeles, California, 759-762
• Country of Publication: United States
• Language: English