Predicting Node Degree Centrality with the Node Prominence Profile

TL;DR

This paper introduces a method combining preferential attachment and triadic closure to predict future degree centrality of nodes in social networks, revealing early-stage signatures that forecast their importance.

Contribution

It proposes the node prominence profile, a novel approach that effectively predicts future degree centrality by capturing a node's prominence in evolving social networks.

Findings

The method accurately predicts future degree centrality across multiple social networks.

Early-stage nodes exhibit distinctive signatures in their degree centrality trends.

The prominence profile outperforms traditional centrality measures in prediction tasks.

Abstract

Centrality of a node measures its relative importance within a network. There are a number of applications of centrality, including inferring the influence or success of an individual in a social network, and the resulting social network dynamics. While we can compute the centrality of any node in a given network snapshot, a number of applications are also interested in knowing the potential importance of an individual in the future. However, current centrality is not necessarily an effective predictor of future centrality. While there are different measures of centrality, we focus on degree centrality in this paper. We develop a method that reconciles preferential attachment and triadic closure to capture a node's prominence profile. We show that the proposed node prominence profile method is an effective predictor of degree centrality. Notably, our analysis reveals that individuals in the early stage of evolution display a distinctive and robust signature in degree centrality trend, adequately predicted by their prominence profile. We evaluate our work across four real-world social networks. Our findings have important implications for the applications that require prediction of a node's future degree centrality, as well as the study of social network dynamics.