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Scalable Mining of Maximal Quasi-Cliques: An Algorithm-System Codesign Approach

Summary: Algorithm-system codesign for scalable maximal quasi-clique mining using a redesigned G-thinker with long-task prioritization and timeout-based load balancing. Adapts Quick to the framework, achieving up to 201× speedup on a 3.77M-vertex, 16.5M-edge graph. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12576
Venue
VLDB
Year
2021
Pagerank
5.6627295e-05
Overall Rank
5,149 | 64.19%
DOI
10.14778/3436905.3436916

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Showing 7 of 7 cited papers.

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

Rank Cited Paper Year Venue Pagerank
370 Online Search of Overlapping Communities 2013 SIGMOD 0.00025415479
840 Efficiently Mining Long Patterns from Databases 1998 SIGMOD 0.00016058396
847 Finding the Maximum Clique in Massive Graphs 2017 VLDB 0.00015993322
891 Maximum Biclique Search at Billion Scale 2020 VLDB 0.00015564292
1,650 Efficient Enumeration of Maximal k-Plexes 2015 SIGMOD 0.00011013428
4,867 Application Driven Graph Partitioning 2020 SIGMOD 5.8651797e-05
6,207 Efficiently Computing k-Edge Connected Components via Graph Decomposition 2013 SIGMOD 5.1572428e-05
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