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On Density-based Local Community Search

Summary: Proposes density-based Local Community Search (LCS) with seed set R, optimizing f(S) over induced subgraphs to blend density/conductance with R-inclusion, aiming for strongly local computation. Introduces a configuration-based framework; for density-based LCS, it identifies C_L as configurations enabling strongly local optimization and provides an LP-based, deployable solution. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
1919
Venue
PODS
Year
2024
Pagerank
4.613363e-05
Overall Rank
7,986 | 44.45%
DOI
10.1145/3651589

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
3,575 Finding Locally Densest Subgraphs: A Convex Programming Approach 2022 VLDB 6.9528126e-05
4,875 Discovering Polarization Niches via Dense Subgraphs with Attractors and Repulsers 2022 VLDB 5.8594122e-05
5,355 Anchored Densest Subgraph 2022 SIGMOD 5.5517073e-05
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