检验社交网络社区搜索算法的心理凝聚力，发现现有方法效果不佳。
How Cohesive Are Community Search Results on Online Social Networks?: An Experimental Evaluation
- 引入五种基于社会心理学的凝聚力衡量方式。
- 实验证明结构与心理凝聚力无明显相关性。
- 适合关注社交网络社区质量评估的研究者。
近年来，大量针对大规模图的社区搜索方法被提出，其核心在于定义和度量凝聚力。本文针对在线社交网络场景，实验评估了这些社区搜索算法在凝聚力方面的有效性。社交社区的形成与发展受群体凝聚力理论影响，该理论在社会心理学中已有广泛研究。然而，当前通用方法通常采用结构或属性特征衡量凝聚力，忽略了领域特性的群体凝聚力概念。为此，本文基于社会心理学中的群体凝聚力概念，提出五种新型心理导向的凝聚力度量，并构建名为CHASE的新框架，用于评估八种代表性社区搜索算法在在线社交网络上的表现。分析结果显示，结构凝聚力与心理凝聚力之间不存在明确相关性，且没有一种算法能有效识别出心理上高度凝聚的社区。该研究为未来社区搜索方法的设计提供了新视角。
原文摘要 · Abstract (English)
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.
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