iGraph in Action: Performance Analysis of Disk-Based Graph Indexing Techniques
Summary: iGraph implements disk-based graph indexing techniques in a common framework to enable fair, full-disk comparisons rather than relying on authors' binaries. GUI-driven editing of datasets/workloads, query execution, and analytics (best/worst queries, algorithm comparisons, indexing-feature impact). (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Wook-Shin Han
- 2. Minh-Duc Pham
- 3. Jinsoo Lee
- 4. Romans Kasperovics
- 5. Jeffrey Xu Yu
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
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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 |
|---|---|---|---|---|
| 203 | Graph Indexing: A Frequent Structure-based Approach | 2004 | SIGMOD | 0.00034889335 |
| 350 | FG-Index: Towards Verification-Free Query Processing on Graph Databases | 2007 | SIGMOD | 0.00026365067 |
| 612 | Taming Verification Hardness: An Efficient Algorithm for Testing Subgraph Isomorphism | 2008 | VLDB | 0.0001920234 |
| 900 | Graph Indexing: Tree + Delta >= Graph | 2007 | VLDB | 0.00015495155 |
| 3,720 | iGraph: A Framework for Comparisons of Disk-Based Graph Indexing Techniques | 2010 | VLDB | 6.8192237e-05 |
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