Finding Locally Densest Subgraphs: A Convex Programming Approach
Summary: Introduces a convex-programming algorithm for locally densest subgraphs, uncovering multiple dense regions rather than only the global densest subgraph. Powerful pruning yields up to 10,000× speedups over prior LDS methods on billion-edge graphs. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Chenhao Ma (University of Hong Kong)
- 2. Reynold Cheng (University of Hong Kong)
- 3. Laks V.S. Lakshmanan (University of British Columbia)
- 4. Xiaolin Han (University of Hong Kong)
BibTeX Citation
@article{ma_vldb22,
title = {{Finding Locally Densest Subgraphs: A Convex Programming Approach}},
author = {Ma, Chenhao and Cheng, Reynold and Lakshmanan, Laks V.S. and Han, Xiaolin},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {11},
pages = {2719--2732},
doi = {10.14778/3551793.3551826},
url = {https://doi.org/10.14778/3551793.3551826},
year = {2022}
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