Efficient Exact and Approximate Betweenness Centrality Computation for Temporal Graphs
Author:
Affiliation:
1. Zhejiang University of Technology, Hangzhou, China
2. Zhejiang University, Hangzhou, China
3. University of New South Wales, Sydney, Australia
Funder
National Natural Science Foundation of China
Key Research Project of Zhejiang Province
Natural Science Foundation of Zhejiang Province
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3589334.3645438
Reference46 articles.
1. A Benchmark for Betweenness Centrality Approximation Algorithms on Large Graphs
2. Centrality Measures on Big Graphs
3. KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation
4. A faster algorithm for betweenness centrality*
5. CENTRALITY ESTIMATION IN LARGE NETWORKS
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1. Making Temporal Betweenness Computation Faster and Restless;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24
2. Efficient Betweenness Centrality Computation over Large Heterogeneous Information Networks;Proceedings of the VLDB Endowment;2024-07
3. MANTRA: Temporal Betweenness Centrality Approximation Through Sampling;Lecture Notes in Computer Science;2024
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