Community Structures in Information Networks for a Discrete Agent Population

TL;DR

This paper introduces a mathematical model for community formation in social information networks with discrete agents, extending previous continuous models to better reflect real-world network structures and behaviors.

Contribution

It generalizes existing models to discrete agent populations, providing insights into community structure, connectivity, and information flow in realistic social networks.

Findings

Characterizes community structures as Nash equilibria.

Highlights differences between discrete and continuous agent models.

Provides a framework for analyzing information dissemination within communities.

Abstract

Communities are an important feature of social networks. The goal of this paper is to propose a mathematical model to study the community structure in social networks. For this, we consider a particular case of a social network, namely information networks. We assume that there is a population of agents who are interested in obtaining content. Agents differ in the type of content they are interested in. The goal of agents is to form communities in order to maximize their utility for obtaining and producing content. We use this model to characterize the structure of communities that emerge as a Nash equilibrium in this setting. The work presented in this paper generalizes results in the literature that were obtained for the case of a continuous agent model, to the case of a discrete agent population model. We note that a discrete agent set reflects more accurately real-life information networks, and are needed in order to get additional insights into the community structure, such as for example the connectivity (graph structure) within in a community, as well as information dissemination within a community.