How Cohesive Are Community Search Results on Online Social Networks?: An Experimental Evaluation

2025-04-28

AI Summary

Existing community search algorithms lack effectiveness in identifying psychologically cohesive communities—groups bound by shared identity, trust, and mutual commitment rather than mere structural or attribute-based proximity. Method: We propose CHASE, the first evaluation framework for community search grounded in social-psychological theories of group cohesion. Unlike conventional structural or attribute-based metrics, CHASE introduces five psychology-inspired quantitative measures and systematically evaluates eight state-of-the-art algorithms on real-world online social networks. Contribution/Results: Empirical results reveal that all evaluated algorithms fail to detect psychological cohesion; structural compactness (e.g., density, conductance) exhibits negligible correlation with psychological cohesion metrics. This work bridges a critical theoretical and methodological gap in community search by establishing the first psychologically interpretable benchmark. It provides both a novel evaluation standard and empirical evidence to guide human-centered algorithm design—particularly for applications involving behavioral modeling, collective decision-making, and trust-aware network analysis.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Evaluation and Analysis
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Social Networks and Social Media: Social mining and social search on the Web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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 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.

Problem

• Evaluating cohesiveness of community search algorithms in social networks
• Introducing psychology-based measures for group cohesion in communities
• Assessing correlation between structural and psychological cohesiveness in networks

Innovation

• Introduces psychology-informed cohesiveness measures
• Proposes CHASE framework for evaluation
• Evaluates eight community search algorithms