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Faster Betweenness Centrality Updates in Evolving Networks.

, , , and . SEA, volume 75 of LIPIcs, page 23:1-23:16. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2017)

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Approximating Betweenness Centrality in Large Evolving Networks., , and . ALENEX, page 133-146. SIAM, (2015)Computing Top-k Closeness Centrality in Fully Dynamic Graphs., , , and . Massive Graph Analytics, Chapman and Hall/CRC, (2022)CNVScan: detecting borderline copy number variations in NGS data via scan statistics., , , and . BCB, page 335-344. ACM, (2015)Scaling up Group Closeness Maximization., , and . ALENEX, page 209-222. SIAM, (2018)Algorithms for the Identification of Central Nodes in Large Real-World Networks. Karlsruhe University, Germany, (2018)base-search.net (ftubkarlsruhe:oai:EVASTAR-Karlsruhe.de:1000085647).Computing Top-k Closeness Centrality Faster in Unweighted Graphs., , , , and . ALENEX, page 68-80. SIAM, (2016)Estimating Current-Flow Closeness Centrality with a Multigrid Laplacian Solver., , , and . CSC, page 1-12. SIAM, (2016)Computing Top-k Closeness Centrality in Fully-dynamic Graphs., , , and . ALENEX, page 21-35. SIAM, (2018)Faster Betweenness Centrality Updates in Evolving Networks., , , and . SEA, volume 75 of LIPIcs, page 23:1-23:16. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2017)Fully-Dynamic Approximation of Betweenness Centrality., and . ESA, volume 9294 of Lecture Notes in Computer Science, page 155-166. Springer, (2015)