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Mining Graph Patterns Efficiently via Randomized Summaries

Summary: Proposes Summarize-Mine, a graph-pattern mining framework that compresses within-transaction graphs with randomized summaries to cut embedding enumeration costs. Repeating with probabilistic guarantees reduces pattern loss, enabling malware fingerprints. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9948
Venue
VLDB
Year
2009
Pagerank
5.9694403e-05
Overall Rank
4,713 | 67.25%
DOI
-

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Showing 8 of 8 cited papers.

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

Rank Cited Paper Year Venue Pagerank
182 Mining Frequent Patterns without Candidate Generation 2000 SIGMOD 0.00036955562
202 Graph Indexing: A Frequent Structure-based Approach 2004 SIGMOD 0.00034881375
387 Graph Summarization with Bounded Error 2008 SIGMOD 0.00024682268
430 Approximate Query Processing: Taming the TeraBytes! A Tutorial 2001 VLDB 0.00023406426
435 Efficient Aggregation for Graph Summarization 2008 SIGMOD 0.00023268266
473 Sampling Large Databases for Association Rules 1996 VLDB 0.00022304724
1,720 Mining Significant Graph Patterns by Leap Search 2008 SIGMOD 0.00010757565
7,243 Finding Relevant Patterns in Bursty Sequences 2008 VLDB 4.7859835e-05
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