Finding frequent patterns in a large sparse graph

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Finding frequent patterns in a large sparse graph (2004)

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by Michihiro Kuramochi , George Karypis

Venue:	SIAM Data Mining Conference
Citations:	128 - 4 self

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@INPROCEEDINGS{Kuramochi04findingfrequent,
    author = {Michihiro Kuramochi and George Karypis},
    title = {Finding frequent patterns in a large sparse graph},
    booktitle = {SIAM Data Mining Conference},
    year = {2004}
}

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Abstract

This paper presents two algorithms based on the horizontal and vertical pattern discovery paradigms that find the connected subgraphs that have a sufficient number of edge-disjoint embeddings in a single large undirected labeled sparse graph. These algorithms use three different methods to determine the number of the edge-disjoint embeddings of a subgraph that are based on approximate and exact maximum independent set computations and use it to prune infrequent subgraphs. Experimental evaluation on real datasets from various domains show that both algorithms achieve good performance, scale well to sparse input graphs with more than 100,000 vertices, and significantly outperform a previously developed algorithm.

Keyphrases

large sparse graph frequent pattern edge-disjoint embeddings good performance infrequent subgraphs various domain different method connected subgraphs vertical pattern discovery paradigm sparse graph experimental evaluation sufficient number input graph scale well real datasets

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