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Accelerating Core Decomposition in Billion-Scale Hypergraphs

Summary: Efficient k-core decomposition for billion-scale hypergraphs. Introduces novel core-value computation and redundancy-elimination to reduce memory 36x and deliver 7x speedup, enabling single-thread processing of billion-edge hypergraphs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7005
Venue
SIGMOD
Year
2025
Pagerank
4.3849295e-05
Overall Rank
9,146 | 36.38%
DOI
10.1145/3709656

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,311 Efficient Temporal Edge-Core Maintenance in Streaming Graphs 2026 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

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

Rank Cited Paper Year Venue Pagerank
353 Local Search of Communities in Large Graphs 2014 SIGMOD 0.00026277992
738 Large Scale Cohesive Subgraphs Discovery for Social Network Visual Analysis 2013 VLDB 0.00017435236
966 Streaming Algorithms for k-core Decomposition 2013 VLDB 0.00014960672
1,150 K-Core Decomposition of Large Networks on a Single PC 2016 VLDB 0.00013657353
4,139 On Querying Historical K-Cores 2021 VLDB 6.415046e-05
5,589 Neighborhood-based Hypergraph Core Decomposition 2023 VLDB 5.4216989e-05
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