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)
Incoming Non-self Citations Over Time
Authors
- 1. Chen Chen (University of Illinois Urbana-Champaign)
- 2. Cindy X. Lin (University of Illinois Urbana-Champaign)
- 3. Matt Fredrikson (University of Wisconsin)
- 4. Mihai Christodorescu (IBM)
- 5. Xifeng Yan (University of California Santa Barbara)
- 6. Jiawei Han (University of Illinois Urbana-Champaign)
BibTeX Citation
@article{chen_vldb09,
title = {{Mining Graph Patterns Efficiently via Randomized Summaries}},
author = {Chen, Chen and Lin, Cindy X. and Fredrikson, Matt and Christodorescu, Mihai and Yan, Xifeng and Han, Jiawei},
journal = {PVLDB},
series = {{VLDB} '09},
doi = {10.14778/1687627.1687711},
url = {https://doi.org/10.14778/1687627.1687711},
year = {2009}
}
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,628 | Summarizing Static and Dynamic Big Graphs | 2017 | VLDB | 6.0438005e-05 |
| 6,792 | GAIA: Graph Classification Using Evolutionary Computation | 2010 | SIGMOD | 5.664537e-05 |
| 8,358 | Mining Top-k Pairs of Correlated Subgraphs in a Large Network | 2020 | VLDB | 5.3314609e-05 |
| 8,904 | Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability | 2024 | SIGMOD | 5.238088e-05 |
| 11,635 | Efficient Discovery of Significant Patterns with Few-Shot Resampling | 2024 | VLDB | 4.9605343e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
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 |
|---|---|---|---|---|
| 165 | Mining Frequent Patterns without Candidate Generation | 2000 | SIGMOD | 0.00027323925 |
| 176 | Graph Indexing: A Frequent Structure-based Approach | 2004 | SIGMOD | 0.00026634326 |
| 374 | Approximate Query Processing: Taming the TeraBytes! A Tutorial | 2001 | VLDB | 0.00019698889 |
| 477 | Sampling Large Databases for Association Rules | 1996 | VLDB | 0.00017624579 |
| 499 | Efficient Aggregation for Graph Summarization | 2008 | SIGMOD | 0.00017292147 |
| 563 | Graph Summarization with Bounded Error | 2008 | SIGMOD | 0.00016294243 |
| 2,022 | Mining Significant Graph Patterns by Leap Search | 2008 | SIGMOD | 9.1569678e-05 |
| 7,862 | Finding Relevant Patterns in Bursty Sequences | 2008 | VLDB | 5.4221815e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 13,826 | The Power of Summarization in Graph Mining and Learning: Smaller Data, Faster Methods, More Interpretability | 2021 | VLDB |
| 2 | 5,102 | Efficient Graph Summarization using Weighted LSH at Billion-Scale | 2021 | SIGMOD |
| 3 | 2,022 | Mining Significant Graph Patterns by Leap Search | 2008 | SIGMOD |
| 4 | 4,363 | Mining Top-K Large Structural Patterns in a Massive Network | 2011 | VLDB |
| 5 | 8,358 | Mining Top-k Pairs of Correlated Subgraphs in a Large Network | 2020 | VLDB |
| 6 | 5,744 | Output Space Sampling for Graph Patterns | 2009 | VLDB |
| 7 | 5,499 | Towards Proximity Pattern Mining in Large Graphs | 2010 | SIGMOD |
| 8 | 2,771 | GraphMiner: A Structural Pattern-Mining System for Large Disk-based Graph Databases and Its Applications | 2005 | SIGMOD |
| 9 | 7,629 | Mining Attribute-structure Correlated Patterns in Large Attributed Graphs | 2012 | VLDB |
| 10 | 4,323 | On Dense Pattern Mining in Graph Streams | 2010 | VLDB |