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 |
|---|---|---|---|---|
| 2,896 | Online Density Bursting Subgraph Detection from Temporal Graphs | 2019 | VLDB | 7.9828452e-05 |
| 11,866 | Finding Theme Communities from Database Networks | 2019 | VLDB | 5.093636e-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 |
|---|---|---|---|---|
| 1,018 | Dense Subgraph Maintenance under Streaming Edge Weight Updates for Real-time Story Identification | 2012 | VLDB | 0.0001263063 |
| 2,085 | Scalable K-Means++ | 2012 | VLDB | 9.1943614e-05 |
| 3,862 | CSV: Visualizing and Mining Cohesive Subgraphs | 2008 | SIGMOD | 7.0665158e-05 |
| 7,933 | PAnG - Finding Patterns in Annotation Graphs | 2012 | SIGMOD | 5.5181056e-05 |
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