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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
10493
Venue
VLDB
Year
2012
Pagerank
4.6947636e-05
Overall Rank
7,614 | 47.04%
DOI
-

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