Discovering cohesive groups is a fundamental primitive in graph-based recommender systems, underpinning tasks such as social recommendation, bundle discovery, and community-aware modeling. In interaction graphs, cohesion is often modeled as the $γ$-quasi-clique, an induced subgraph whose internal edge density meets a user-defined threshold $γ$. This formulation provides explicit control over within-group connectivity while accommodating the sparsity inherent in real-world data. This paper presents EDQC, an effective framework for cohesive group discovery under explicit density constraints. EDQC leverages a lightweight energy diffusion process to rank vertices for localizing promising candidate regions. Guided by this ranking, the framework extracts and refines a candidate subgraph to ensure the output strictly satisfies the target density requirement. Extensive experiments on 75 real-world graphs across varying density thresholds demonstrate that EDQC identifies the largest mean $γ$-quasi-cliques in the vast majority of cases, achieving lower variance than the state-of-the-art methods while maintaining competitive runtime. Furthermore, statistical analysis confirms that EDQC significantly outperforms the baselines, underscoring its robustness and practical utility for cohesive group discovery in graph-based recommender systems.

翻译：在基于图的推荐系统中，发现凝聚群组是一项基础性任务，支撑着社交推荐、捆绑发现和社区感知建模等应用。在交互图中，凝聚性通常被建模为$γ$-准团，即内部边密度满足用户定义阈值$γ$的诱导子图。该模型在适应现实数据固有稀疏性的同时，为群组内部连通性提供了显式控制。本文提出EDQC，一种在显式密度约束下进行凝聚群组发现的有效框架。EDQC利用轻量级能量扩散过程对顶点进行排序，以定位有潜力的候选区域。在此排序引导下，框架提取并优化候选子图，确保输出严格满足目标密度要求。在75个真实世界图数据上针对不同密度阈值的大量实验表明，EDQC在绝大多数情况下能识别出平均规模最大的$γ$-准团，在保持具有竞争力的运行时间的同时，其方差低于现有最优方法。此外，统计分析证实EDQC显著优于基线方法，凸显了其在基于图的推荐系统中进行凝聚群组发现的鲁棒性和实用价值。