Fast approximation of betweenness centrality through sampling

• Published in: Data mining and knowledge discovery Vol. 30; no. 2; pp. 438 - 475
• Main Authors: Riondato, Matteo, Kornaropoulos, Evgenios M.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2016; Springer Nature B.V
• Subjects: Algorithms; Approximation; Artificial Intelligence; Chemistry and Earth Sciences; Communications networks; Computer Science; Data mining; Data Mining and Knowledge Discovery; Estimates; Graph theory; Graphs; Information Storage and Retrieval; Mathematical analysis; Networks; Physics; Sample size; Shortest-path problems; Social network analysis; Social networks; Statistics for Engineering; Approximation algorithms; Betweenness centrality; VC-dimension; Social network analysis; Sampling

Betweenness centrality is a fundamental measure in social network analysis, expressing the importance or influence of individual vertices (or edges) in a network in terms of the fraction of shortest paths that pass through them. Since exact computation in large networks is prohibitively expensive, w...