On efficient use of entropy centrality for social network analysis and community detection

•We motivate and interpret entropy centrality metric for path and walk based flows in networks.•We showcase the flexibility of the metric in allowing varying locality analyses.•We present an accurate community detection algorithm supported by experimental tests.

This paper motivates and interprets entropy centrality, the measure understood as the entropy of flow destination in a network. The paper defines a variation of this measure based on a discrete, random Markovian transfer process and showcases its increased utility over the originally introduced path-based network entropy centrality. The re-defined entropy centrality allows for varying locality in centrality analyses, thereby distinguishing locally central and globally central network nodes. It also leads to a flexible and efficient iterative community detection method. Computational experiments for clustering problems with known ground truth showcase the effectiveness of the presented approach.