Paper Info
Title
Percolator: Scalable Pattern Discovery in Dynamic Graphs.
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.
Year
DOI
Venue
2018
10.1145/3159652.3160589
WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining Marina Del Rey CA USA February, 2018
Keywords
Field
DocType
Graph mining, parallel system, data stream
Graph,Data mining,Data stream,Computer science,Pattern detection,Scalability
Conference
ISBN
Citations 
PageRank 
978-1-4503-5581-0
0
0.34
References 
Authors
5
6
Name
Order
Citations
PageRank
Sutanay Choudhury1152.64
Purohit Sumit2245.22
peng lin33912.10
Yinghui Wu482442.79
Lawrence B. Holder51448259.29
Khushbu Agarwal6163.94