Distinctiveness Centrality in Social Networks

A. Fronzetti Colladon, M. Naldi
12/6/2019
cs.SIphysics.soc-ph

Official code: iandreafc/distinctiveness (8 stars, MIT, last commit 2023-10-15).

Abstract

The determination of node centrality is a fundamental topic in social network studies. As an addition to established metrics, which identify central nodes based on their brokerage power, the number and weight of their connections, and the ability to quickly reach all other nodes, we introduce five new measures of Distinctiveness Centrality. These new metrics attribute a higher score to nodes keeping a connection with the network periphery. They penalize links to highly-connected nodes and serve the identification of social actors with more distinctive network ties. We discuss some possible applications and properties of these newly introduced metrics, such as their upper and lower bounds. Distinctiveness centrality provides a viewpoint of centrality alternative to that of established metrics.

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Code Implementations(1)

iandreafc/distinctivenessOfficial100%
MIT

A Python package to calculate Distinctiveness Centrality in social networks

81Python, Jupyter NotebookJul 17, 20193 years agoMIT

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Cite this paper

@article{colladon2019distinctiveness,
  title  = {Distinctiveness Centrality in Social Networks},
  author = {A. Fronzetti Colladon and M. Naldi},
  year   = {2019},
  eprint = {1912.03391},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1912.03391}
}

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