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Flexible and Feasible Support Measures for Mining Frequent Patterns in Large Labeled Graphs

Summary: Proposes a unified hypergraph framework for support measures in single-graph mining. Introduces MI and MVC measures; MI is linear-time computable; min-image-based measure bounds MI; MVC NP-hard but constant-factor approximable, with relaxations and bounds. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5302
Venue
SIGMOD
Year
2017
Pagerank
4.3254416e-05
Overall Rank
9,565 | 33.46%
DOI
10.1145/3035918.3035936

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,848 Efficient Top-k Frequent Subgraph Mining Using Tight Upper and Lower Bounds 2025 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
1,089 GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph 2014 VLDB 0.00014157922
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