Rethinking Centrality: The Role of Dynamical Processes in Social Network Analysis

TL;DR

This paper critiques traditional social network centrality measures based on random walks, proposing a classification of dynamical processes and demonstrating that non-conservative Alpha-Centrality better aligns with empirical data in broadcasting networks.

Contribution

It introduces a classification of dynamical processes into conservative and non-conservative types and links them to centrality measures, showing the superiority of Alpha-Centrality in certain social network contexts.

Findings

Non-conservative Alpha-Centrality aligns better with empirical rankings.

Traditional PageRank may not suit all social phenomena.

Classification of dynamical processes informs better network analysis.

Abstract

Many popular measures used in social network analysis, including centrality, are based on the random walk. The random walk is a model of a stochastic process where a node interacts with one other node at a time. However, the random walk may not be appropriate for modeling social phenomena, including epidemics and information diffusion, in which one node may interact with many others at the same time, for example, by broadcasting the virus or information to its neighbors. To produce meaningful results, social network analysis algorithms have to take into account the nature of interactions between the nodes. In this paper we classify dynamical processes as conservative and non-conservative and relate them to well-known measures of centrality used in network analysis: PageRank and Alpha-Centrality. We demonstrate, by ranking users in online social networks used for broadcasting information, that non-conservative Alpha-Centrality generally leads to a better agreement with an empirical ranking scheme than the conservative PageRank.