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Efficient Historical Butterfly Counting in Large Temporal Bipartite Networks via Graph Structure-aware Index

Summary: Proposes the first graph-structure-aware indexing for historical butterfly counting in temporal bipartite networks, combining two novel indices whose space scales with counts of butterflies and wedges. Adds index compression and unbiased approximation, proves asymptotic gains on power-law graphs, and achieves up to 10^5x query speedups with modest memory. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13823
Venue
VLDB
Year
2025
Pagerank
4.1945683e-05
Overall Rank
10,563 | 26.52%
DOI
10.14778/3725688.3725693

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Rank Citing Paper Year Venue Pagerank
10,300 Scalable Approximate Biclique Counting over Large Bipartite Graphs 2026 VLDB 4.1945683e-05
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