Modeling Link Recommendations as a Network Growth Mechanism and Their Impact on Social Contagion

Abstract

Link recommendation algorithms significantly shape online social networks, in- fluencing both their structural evolution and critical processes such as informa- tion and behavior spread. This paper investigates how these algorithms affect simple and complex contagion processes by modeling recommendations as addi- tional network growth mechanisms. We introduce a synthetic network model that integrates preferential attachment, triadic closure, and choice homophily, then ex- tend it with various link recommenders, including heuristics and graph neural net- works (GNNs). Our findings show that while simple contagions exhibit relatively modest shifts under most recommenders, complex contagions are highly sensitive to clustering- and homophily-based recommendations, thriving at moderate rec- ommendation strengths but sharply diminishing under excessive recommendation strength. These results underscore the nuanced interplay between network struc- ture, recommendation strength, and contagion dynamics, highlighting the impor- tance of incorporating social contagions into the design of link recommendation algorithms