Mining Top-k Pairs of Correlated Subgraphs in a Large Network
Summary: Introduces correlated subgraph mining, targeting top-k pairs whose instances co-occur nearby—unlike conventional frequent-subgraph mining. Best-first exact/approximate search, top-k pruning, and the on-demand compressed Replica structure enable scalable correlation computation. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Arneish Prateek (Indian Institute of Technology Delhi)
- 2. Arijit Khan (Nanyang Technological University)
- 3. Akshit Goyal (Indian Institute of Technology Delhi)
- 4. Sayan Ranu (Indian Institute of Technology Delhi)
BibTeX Citation
@article{prateek_vldb20,
title = {{Mining Top-k Pairs of Correlated Subgraphs in a Large Network}},
author = {Prateek, Arneish and Khan, Arijit and Goyal, Akshit and Ranu, Sayan},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {9},
pages = {1511--1524},
doi = {10.14778/3397230.3397245},
url = {https://doi.org/10.14778/3397230.3397245},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,072 | Efficient Top-k Frequent Subgraph Mining Using Tight Upper and Lower Bounds | 2025 | VLDB | 5.093636e-05 |
| 11,408 | Closest Pairs Search Over Data Stream | 2023 | SIGMOD | 5.093636e-05 |
| 11,583 | Answering Regular Path Queries through Exemplars | 2022 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 18 of 18 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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