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)
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Authors
- 1. Ziyu Guan
- 2. Xifeng Yan
- 3. Lance M. Kaplan
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 313 | Graph Clustering Based on Structural/Attribute Similarities | 2009 | VLDB | 0.0002809849 |
| 2,930 | Assessing and Ranking Structural Correlations in Graphs | 2011 | SIGMOD | 7.8790753e-05 |
| 3,653 | The Priority R-Tree: A Practically Efficient and Worst-Case Optimal R-Tree | 2004 | SIGMOD | 6.8725114e-05 |
| 5,158 | Towards Proximity Pattern Mining in Large Graphs | 2010 | SIGMOD | 5.6510256e-05 |
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