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Percolator: Scalable Pattern Discovery in Dynamic Graphs

Authors:

Khushbu AgarwalAuthors Info & Claims

WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

Pages 759 - 762

https://doi.org/10.1145/3159652.3160589

Published: 02 February 2018 Publication History

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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.

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Cited By

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Choudhury SAgarwal KHam CTamang S(2023)MediSage: An AI Assistant for Healthcare via Composition of Neural-Symbolic Reasoning OperatorsCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587361(258-261)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587361
Haghir Chehreghani M(2020)Dynamical algorithms for data mining and machine learning over dynamic graphsWIREs Data Mining and Knowledge Discovery10.1002/widm.139311:2Online publication date: 3-Nov-2020
https://doi.org/10.1002/widm.1393

Index Terms

Percolator: Scalable Pattern Discovery in Dynamic Graphs
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Data stream mining
2. Theory of computation
  1. Design and analysis of algorithms
    1. Graph algorithms analysis
      1. Dynamic graph algorithms

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Published In

WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

February 2018

821 pages

ISBN:9781450355810

DOI:10.1145/3159652

General Chairs:
Yi Chang
Jilin University, Huawei Inc.
,
Chengxiang Zhai
University of Illinois Urbana-Champaign
,
Program Chairs:
Yan Liu
University of Southern California
,
Yoelle Maarek
Amazon

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 February 2018

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WSDM 2018

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WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining

February 5 - 9, 2018

CA, Marina Del Rey, USA

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WSDM '18 Paper Acceptance Rate 81 of 514 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

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Choudhury SAgarwal KHam CTamang S(2023)MediSage: An AI Assistant for Healthcare via Composition of Neural-Symbolic Reasoning OperatorsCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587361(258-261)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587361
Haghir Chehreghani M(2020)Dynamical algorithms for data mining and machine learning over dynamic graphsWIREs Data Mining and Knowledge Discovery10.1002/widm.139311:2Online publication date: 3-Nov-2020
https://doi.org/10.1002/widm.1393

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Discovering Interesting Patterns from Hypergraphs

Mining frequent cross-graph quasi-cliques

Mining Frequent Subgraph Patterns from Uncertain Graph Data