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Table 1 Centrality measures. The centrality measures were represented in five groups depending on their logic and formulae

From: A systematic survey of centrality measures for protein-protein interaction networks

Distance_based Degree-based Eigen-based Neighborhood-based Miscellanous
Average Distance Authority_score Eigenvector centralities ClusterRank Geodesic K-Path Centrality
Barycenter Degree Centrality Katz Centrality (Katz Status Index) Density of Maximum Neighborhood Component (DMNC) Harary Graph Centrality
Closeness Centrality (Freeman) Diffusion Degree Laplacian Centrality Maximum Neighborhood Component (MNC) Information Centrality
Closeness centrality (Latora) Kleinberg’s hub centrality scores   Subgraph centrality scores Markov Centrality
Decay Centrality Leverage Centrality   Shortest-Paths Betweenness Centrality
Eccentricity of the vertices Lobby Index (Centrality)  
Lin Centrality     
Radiality Centrality     
Residual Closeness Centrality     
  1. Note that the first column (i.e. distance-based centralities) was specified according to the definition of distance between vertices in graph theory. The second one (i.e. degree-based centralities) was defined based on the number of immediate neighbors of each node within a given network. Eigen-values of adjacency matrix was the main idea to classify the Eigen-based centralities. Furthermore, the concept of subgraph or community structure was proposed in the neighborhood-based centralities. Others were collected in the miscellaneous group. Remind that this grouping was just applied to have better visualizations.