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Measuring Two-Event Structural Correlations on Graphs

Summary: Novel measure for two-event structural correlations on graphs—reference-node sampling around event nodes and Kendall's tau to quantify concordance of local density shifts. Scalable framework with multiple sampling strategies and asymptotic-normality significance, validated on real networks with synthetic and real events, demonstrating accuracy, efficiency, and scalability. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10376
Venue
VLDB
Year
2012
Pagerank
4.1945683e-05
Overall Rank
12,131 | 15.61%
DOI
-

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

Rank Cited Paper Year Venue Pagerank
313 Graph Clustering Based on Structural/Attribute Similarities 2009 VLDB 0.00028097557
2,930 Assessing and Ranking Structural Correlations in Graphs 2011 SIGMOD 7.8723983e-05
3,650 The Priority R-Tree: A Practically Efficient and Worst-Case Optimal R-Tree 2004 SIGMOD 6.8783391e-05
5,159 Towards Proximity Pattern Mining in Large Graphs 2010 SIGMOD 5.6587631e-05
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