Work

Generating social networks with static and dynamic utility-maximization approaches

Year: 2026

Type: article

Abstract: In this paper, we introduce a conceptual framework that models human social networks as an undirected dot-product graph of independent individuals. Their relationships are only determined by a cost-benefit analysis, i.e. by maximizing an objective function at the scale of the individual or of the whole network. On this framework, we build a new artificial network generator in two versions. The first fits within the tradition of artificial network generators by being able to generate similar networks from empirical data. The second relaxes the computational efficiency constraint and implements the same micro-based decision algorithm, but in agent-based simulations with time and fully independent agents. This latter version enables social scientists to perform an in-depth analysis of the consequences of behavioral constraints affecting individuals on the network they form. This point is illustrated by a case study of imperfect information.

Source: Social Network Analysis and Mining

DOI: 10.1007/s13278-026-01582-8

Authors: Aldric Labarthe, Yann Kerzreho

Institutions: Centre National de la Recherche Scientifique, Inserm, Université Paris Cité, Université Paris-Saclay, Centre Borelli, École Normale Supérieure Paris-Saclay

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,190

Funder: Université Paris-Saclay