标题： 实验分析与凝聚子图发现的评估 显示英文标题

标题： Experimental Analysis and Evaluation of Cohesive Subgraph Discovery

摘要： 在网络中检索紧密子图是社会网络分析和图数据管理中的基本问题。 这些子图可以用于营销策略或推荐系统。 尽管多年来引入了许多模型，但对其性能的系统比较，尤其是在不同网络配置下的比较，仍未被探索。 在本研究中，我们通过基于任务的评估方法对各种紧密子图模型进行了评估，并在合成网络和真实网络上进行了广泛的实验研究。 因此，我们揭示了紧密子图模型的特性，强调了它们的效率和适用性。 我们的研究结果不仅提供了对当前模型的详细评估，还通过阐明子图的可解释性与紧密性之间的平衡，为未来的研究奠定了基础。 这项研究指导了针对特定分析需求和应用选择合适模型，提供了有价值的见解。 显示英文摘要

摘要： Retrieving cohesive subgraphs in networks is a fundamental problem in social network analysis and graph data management. These subgraphs can be used for marketing strategies or recommendation systems. Despite the introduction of numerous models over the years, a systematic comparison of their performance, especially across varied network configurations, remains unexplored. In this study, we evaluated various cohesive subgraph models using task-based evaluations and conducted extensive experimental studies on both synthetic and real-world networks. Thus, we unveil the characteristics of cohesive subgraph models, highlighting their efficiency and applicability. Our findings not only provide a detailed evaluation of current models but also lay the groundwork for future research by shedding light on the balance between the interpretability and cohesion of the subgraphs. This research guides the selection of suitable models for specific analytical needs and applications, providing valuable insights.