research-article

Output space sampling for graph patterns

Authors:

Mohammad Al Hasan,

Mohammed J. ZakiAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 2, Issue 1

Pages 730 - 741

https://doi.org/10.14778/1687627.1687710

Published: 01 August 2009 Publication History

Abstract

Recent interest in graph pattern mining has shifted from finding all frequent subgraphs to obtaining a small subset of frequent subgraphs that are representative, discriminative or significant. The main motivation behind that is to cope with the scalability problem that the graph mining algorithms suffer when mining databases of large graphs. Another motivation is to obtain a succinct output set that is informative and useful. In the same spirit, researchers also proposed sampling based algorithms that sample the output space of the frequent patterns to obtain representative subgraphs. In this work, we propose a generic sampling framework that is based on Metropolis-Hastings algorithm to sample the output space of frequent subgraphs. Our experiments on various sampling strategies show the versatility, utility and efficiency of the proposed sampling approach.

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Output space sampling for graph patterns

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 2, Issue 1

August 2009

1293 pages

ISSN:2150-8097

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VLDB Endowment

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Published: 01 August 2009

Published in PVLDB Volume 2, Issue 1

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Jha VTripathi P(2024)Conscious points and patterns extraction: a high-performance computing model for knowledge discovery in cognitive IoTThe Journal of Supercomputing10.1007/s11227-024-06348-780:17(24871-24907)Online publication date: 1-Nov-2024
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Loglisci CImpedovo ACalders TCeci M(2024)Heuristic approaches for non-exhaustive pattern-based change detection in dynamic networksJournal of Intelligent Information Systems10.1007/s10844-024-00866-962:5(1455-1492)Online publication date: 1-Oct-2024
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