A Python package to calculate Distinctiveness Centrality in social and complex networks.

Distinctiveness Centrality is a set of 5 network metrics that attribute larger importance to distinctive connections, i.e. to nodes which have links to loosely connected peers. Revisiting degree and weighted degree centrality, all these metrics penalize connections to hubs or nodes that are very well connected. Distinctiveness measures might serve the identification of strategic social actors, for example those with peripheral connections that keep the network together, avoiding fragmentation.

See the functions page for more information.


Install by running:

pip install -U distinctiveness


Chek the functions page for details.


A tutorial is available here.


Source Code: github.com/iandreafc/distinctiveness

Please cite as

Fronzetti Colladon, A., & Naldi, M. (2020). Distinctiveness Centrality in Social Networks. PLoS ONE, 15(5), e0233276. https://doi.org/10.1371/journal.pone.0233276


This project is licensed under the MIT license.