Recently, numerous community search methods for large graphs have been proposed, at the core of which is defining and measuring cohesion. This paper experimentally evaluates the effectiveness of these community search algorithms w.r.t. cohesiveness in the context of online social networks. Social communities are formed and developed under the influence of group cohesion theory, which has been extensively studied in social psychology. However, current generic methods typically measure cohesiveness using structural or attribute-based approaches and overlook domain-specific concepts such as group cohesion. We introduce five novel psychology-informed cohesiveness measures, based on the concept of group cohesion from social psychology, and propose a novel framework called CHASE for evaluating eight representative CS algorithms w.r.t. these measures on online social networks. Our analysis reveals that there is no clear correlation between structural and psychological cohesiveness, and no algorithm effectively identifies psychologically cohesive communities in online social networks. This study provides new insights that could guide the development of future community search methods.
View on arXiv@article{zhao2025_2504.19489,
title={ How Cohesive Are Community Search Results on Online Social Networks?: An Experimental Evaluation },
author={ Yining Zhao and Sourav S Bhowmick and Nastassja L. Fischer and SH Annabel Chen },
journal={arXiv preprint arXiv:2504.19489},
year={ 2025 }
}