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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
9947
Venue
VLDB
Year
2009
Pagerank
5.9755569e-05
Overall Rank
4,716 | 67.20%
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
181 Mining Frequent Patterns without Candidate Generation 2000 SIGMOD 0.00036992674
203 Graph Indexing: A Frequent Structure-based Approach 2004 SIGMOD 0.00034889335
388 Graph Summarization with Bounded Error 2008 SIGMOD 0.00024662272
435 Efficient Aggregation for Graph Summarization 2008 SIGMOD 0.00023260172
449 Approximate Query Processing: Taming the TeraBytes! A Tutorial 2001 VLDB 0.00022846068
473 Sampling Large Databases for Association Rules 1996 VLDB 0.0002233798
1,747 Mining Significant Graph Patterns by Leap Search 2008 SIGMOD 0.00010691242
7,246 Finding Relevant Patterns in Bursty Sequences 2008 VLDB 4.790704e-05
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