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In this article we introduce an entropy-based, scale-dependent centralitythat is evaluated as the Shannon entropy of the distribution at time t of acontinuous-time random walk. It ranks nodes as a function of the time t, whichacts as a parameter and defines the scale of the network. It is able capturewell-known centralities such as degree, eigenvector and closeness depending onthe range of t. We compare it with the broad class of total $f$-communicabilitycentralities, of which both Katz centrality and total communicability areparticular cases.

An entropy-based, scale-dependent centrality