Process / pipeline
Centrality Analysis — Degree, Betweenness, Eigenvector
Centrality analysis is a family of network-analytic measures, formalized by Freeman (1979), that quantifies the structural importance of individual nodes within a graph. Each centrality index captures a distinct mechanism of influence: degree centrality reflects direct connectivity, betweenness centrality identifies nodes that broker information flow, closeness centrality captures proximity to all others, and eigenvector centrality (along with PageRank) rewards connection to highly connected neighbors.
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Sources
- Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239. DOI: 10.1016/0378-8733(78)90021-7 ↗
- Borgatti, S.P. (2005). Centrality and Network Flow. Social Networks, 27(1), 55-71. DOI: 10.1016/j.socnet.2004.11.008 ↗
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Referenced by
Community DetectionEgo Network AnalysisGraph Neural Network (Network Analysis)k-Core DecompositionLink PredictionMultilayer Network AnalysisNetwork Diffusion ModelsNetwork EconometricsNetwork EmbeddingNetwork Resilience AnalysisPageRankSmall-World and Scale-Free Network AnalysisTemporal Network Analysis