MAGE: Matching approximate patterns in richly-attributed graphs

Robert Pienta, Acar Tamersoy, Hanghang Tong, Duen Horng Chau

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

Given a large graph with millions of nodes and edges, say a social network where both its nodes and edges have multiple attributes (e.g., job titles, tie strengths), how to quickly find subgraphs of interest (e.g., a ring of businessmen with strong ties)? We present MAGE, a scalable, multicore subgraph matching approach that supports expressive queries over large, richly-attributed graphs. Our major contributions include: (1) MAGE supports graphs with both node and edge attributes (most existing approaches handle either one, but not both); (2) it supports expressive queries, allowing multiple attributes on an edge, wildcards as attribute values (i.e., match any permissible values), and attributes with continuous values; and (3) it is scalable, supporting graphs with several hundred million edges. We demonstrate MAGE's effectiveness and scalability via extensive experiments on large real and synthetic graphs, such as a Google+ social network with 460 million edges.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages585-590
Number of pages6
ISBN (Print)9781479956654
DOIs
StatePublished - Jan 7 2015
Event2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States
Duration: Oct 27 2014Oct 30 2014

Other

Other2nd IEEE International Conference on Big Data, IEEE Big Data 2014
CountryUnited States
CityWashington
Period10/27/1410/30/14

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Scalability
Experiments

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

Pienta, R., Tamersoy, A., Tong, H., & Chau, D. H. (2015). MAGE: Matching approximate patterns in richly-attributed graphs. In Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014 (pp. 585-590). [7004278] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2014.7004278

MAGE : Matching approximate patterns in richly-attributed graphs. / Pienta, Robert; Tamersoy, Acar; Tong, Hanghang; Chau, Duen Horng.

Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. Institute of Electrical and Electronics Engineers Inc., 2015. p. 585-590 7004278.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Pienta, R, Tamersoy, A, Tong, H & Chau, DH 2015, MAGE: Matching approximate patterns in richly-attributed graphs. in Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014., 7004278, Institute of Electrical and Electronics Engineers Inc., pp. 585-590, 2nd IEEE International Conference on Big Data, IEEE Big Data 2014, Washington, United States, 10/27/14. https://doi.org/10.1109/BigData.2014.7004278
Pienta R, Tamersoy A, Tong H, Chau DH. MAGE: Matching approximate patterns in richly-attributed graphs. In Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. Institute of Electrical and Electronics Engineers Inc. 2015. p. 585-590. 7004278 https://doi.org/10.1109/BigData.2014.7004278
Pienta, Robert ; Tamersoy, Acar ; Tong, Hanghang ; Chau, Duen Horng. / MAGE : Matching approximate patterns in richly-attributed graphs. Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 585-590
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