Work

Modeling link recommendations as a network growth mechanism and their impact on social contagion

Year: 2026

Type: article

Abstract: Link recommendation algorithms significantly shape online social networks, influencing both their structural evolution and critical processes such as the spread of information and behavior. This paper investigates how these algorithms affect simple and complex contagion processes by modeling link recommendations as a network growth mechanism. This allows for the direct comparison between algorithmic curation and organic growth processes like triadic closure and homophily. Using both heuristic and graph neural network-based recommenders, we find that complex contagions are highly sensitive to algorithmic influence. While moderate recommendation strength can amplify their spread, high strength suppresses it. This non-monotonic effect is the result of a structural self-reinforcement process, driven by top- k truncation: recommenders increase local clustering at the expense of global reach (average shortest path length) and strong truncation (small k) amplifies this effect. Our results underscore the nuanced interplay between network structure, recommendation strength, and contagion dynamics, highlighting the importance of incorporating social contagions into the design of link recommendation algorithms.

Source: Online Social Networks and Media

DOI: 10.1016/j.osnem.2026.100354

Authors: Björn Komander, Jesus Cerquides, Jeffrey Chan, Azadeh Alavi

Institutions: Consejo Superior de Investigaciones Científicas, Artificial Intelligence Research Institute, RMIT University

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: $3,490

Funders: Horizon 2020 Framework Programme, H2020 Marie Skłodowska-Curie Actions