How Cohesive Are Community Search Results on Online Social Networks?: An Experimental Evaluation
Yining Zhao, Sourav S Bhowmick, Nastassja L. Fischer, SH Annabel Chen
TL;DR
The paper tackles the problem of evaluating community search results in online social networks through psychology-informed cohesion measures rather than solely structural criteria. It introduces five cohesion measures inspired by social psychology (EI, SIT, CED for ATG-S; GIP, GID for GI-S) and the CHASE framework to systematically compare eight CS algorithms across four real-world Twitter-derived datasets. The experimental results reveal a lack of correlation between structural cohesiveness and psychological cohesion, with learning-based methods underperforming in identifying psychologically cohesive communities. These findings highlight a gap between current CS design and social-psychology concepts, suggesting that future CS methods should integrate psychology-informed criteria and that ground-truth cohesion benchmarks are needed to guide development and evaluation.
Abstract
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 community search 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.