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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






Average Distance


Eigenvector centralities


Geodesic K-Path Centrality


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.