Machine learningNetwork science

Weighted Closeness Centrality

Weighted closeness centrality extends the classic closeness measure to networks where edges carry numerical weights — such as frequency, strength, or cost — by incorporating those weights into shortest-path distances. Nodes that can reach others quickly along strong or efficient connections receive higher scores, making it a richer indicator of information-spreading potential than its binary counterpart.

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Sources

  1. Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI: 10.1016/j.socnet.2010.03.006
  2. Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25(2), 163–177. DOI: 10.1080/0022250X.2001.9990249

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Referenced by

ScholarGateWeighted Closeness Centrality (Weighted Closeness Centrality (Opsahl Generalized Closeness)). Retrieved 2026-06-04 from https://scholargate.app/en/network-analysis/weighted-closeness-centrality