ALID: Scalable Dominant Cluster Detection
Summary: ALID detects dense dominant clusters without materializing the quadratic affinity graph, using ROI-guided localized infection-immunization dynamics and candidate-infective search. It provides quality guarantees with complexity tied to cluster size and scales to 50M SIFT points via Spark parallelization. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Lingyang Chu (Chinese Academy of Sciences)
- 2. Shuhui Wang (Chinese Academy of Sciences)
- 3. Siyuan Liu (Carnegie Mellon University)
- 4. Qingming Huang (University of Chinese Academy of Sciences)
- 5. Jian Pei (Simon Fraser University)
BibTeX Citation
@article{chu_vldb15,
title = {{ALID: Scalable Dominant Cluster Detection}},
author = {Chu, Lingyang and Wang, Shuhui and Liu, Siyuan and Huang, Qingming and Pei, Jian},
journal = {PVLDB},
series = {{VLDB} '15},
volume = {8},
number = {8},
pages = {826--837},
doi = {10.14778/2757807.2757808},
url = {https://doi.org/10.14778/2757807.2757808},
year = {2015}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,947 | Online Density Bursting Subgraph Detection from Temporal Graphs | 2019 | VLDB | 7.8212985e-05 |
| 12,172 | Finding Theme Communities from Database Networks | 2019 | VLDB | 4.9769913e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 988 | Dense Subgraph Maintenance under Streaming Edge Weight Updates for Real-time Story Identification | 2012 | VLDB | 0.00012654453 |
| 2,130 | Scalable K-Means++ | 2012 | VLDB | 8.992151e-05 |
| 3,903 | CSV: Visualizing and Mining Cohesive Subgraphs | 2008 | SIGMOD | 6.9307809e-05 |
| 8,108 | PAnG - Finding Patterns in Annotation Graphs | 2012 | SIGMOD | 5.3917406e-05 |
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