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Mining Attribute-structure Correlated Patterns in Large Attributed Graphs

Summary: Structural correlation pattern mining links attribute sets to dense subgraphs in large attributed graphs. It blends frequent itemset and quasi-clique ideas, uses null-model significance tests, and pruning-driven search, with evaluation on three real-world attributed graphs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10681
Venue
VLDB
Year
2012
Pagerank
5.6104855e-05
Overall Rank
7,466 | 48.78%
DOI
10.14778/2140436.2140441

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{silva_vldb12,
        title = {{Mining Attribute-structure Correlated Patterns in Large Attributed Graphs}},
        author = {Silva, Arlei and Meira, Jr., Wagner and Zaki, Mohammed J.},
        journal = {PVLDB},
        series = {{VLDB} '12},
        volume = {5},
        number = {5},
        pages = {466--477},
        doi = {10.14778/2140436.2140441},
        url = {https://doi.org/10.14778/2140436.2140441},
        year = {2012}
}

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