Introducing Distinctiveness Centrality

Andrea Fronzetti Colladon, Maurizio Naldi


The determination of node centrality is a fundamental topic in social network studies. In contrast 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 (DC). These novel metrics are useful in social network analysis to attribute higher importance to nodes keeping stronger connections with the network periphery. They penalize links to overly-connected nodes. DC also serves the identification of nodes with more distinctive links, i.e. those with less common dyadic relationships. In this work, we present Distinctiveness Centrality for undirected networks and subsequently extend its original definition, considering the case of directed graphs and the possibility to more strongly penalize connections towards overly-connected nodes. We discuss some possible applications and the properties of these newly introduced metrics, such as their upper and lower bounds and their expected values on scale-free networks. In addition, we analyze the rank correlation of DC with other well-known centrality measures and find no perfect information overlap. The results show that the five DC measures all value the role of nodes keeping the network periphery connected, and provide a viewpoint of centrality alternative to that of established metrics.

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