Faster and Efficient Density Decomposition via Proportional Response with Exponential Momentum
Summary: Density decomposition is cast as a Fisher market (edges as buyers, nodes as sellers) using proportional-response updates that converge to equilibrium. A novel exponential momentum (geometric interpolation) accelerates these gradient-like steps, yielding orders-of-magnitude speedups on large graphs for downstream graph-mining tasks. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Quan Xue (University of Hong Kong)
- 2. T-H. Hubert Chan (University of Hong Kong)
BibTeX Citation
@inproceedings{xue_sigmod25,
title = {{Faster and Efficient Density Decomposition via Proportional Response with Exponential Momentum}},
author = {Xue, Quan and Chan, T-H. Hubert},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725405},
url = {https://dl.acm.org/doi/10.1145/3725405},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,407 | Scaling Up Density Decomposition on Massive Graphs | 2027 | SIGMOD | 4.9605343e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 130 | Discovering Large Dense Subgraphs in Massive Graphs | 2005 | VLDB | 0.0003029792 |
| 269 | 3-HOP: A High-Compression Indexing Scheme for Reachability Query | 2009 | SIGMOD | 0.00022625826 |
| 392 | One Trillion Edges: Graph Processing at Facebook-Scale | 2015 | VLDB | 0.0001917435 |
| 978 | Dense Subgraph Maintenance under Streaming Edge Weight Updates for Real-time Story Identification | 2012 | VLDB | 0.00012699423 |
| 1,198 | KClist++: A Simple Algorithm for Finding k-Clique Densest Subgraphs in Large Graphs | 2020 | VLDB | 0.00011541919 |
| 1,995 | Efficient Algorithms for Densest Subgraph Discovery | 2019 | VLDB | 9.2189157e-05 |
| 2,926 | Finding Locally Densest Subgraphs: A Convex Programming Approach | 2022 | VLDB | 7.837861e-05 |
| 3,396 | Scaling Up k-Clique Densest Subgraph Detection | 2023 | SIGMOD | 7.3277458e-05 |
| 3,778 | Efficient Algorithms for Densest Subgraph Discovery on Large Directed Graphs | 2020 | SIGMOD | 7.0037629e-05 |
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