基于密度的属性网络社区检测框架
A density-based framework for community detection in attributed networks
AI总结：
本文提出AttDeCoDe，一种基于属性的密度社区检测框架，用于分析节点特征在群体形成中起核心作用的网络，通过属性空间估计密度以捕捉同质性驱动的聚集并保持结构性连接约束。
摘要：
社交和协作网络中的社区结构往往源于结构机制（如度异质性和领导者驱动吸引力）与节点属性上的同质性之间的复杂相互作用。现有的社区检测方法通常孤立地关注这些维度，限制了在存在这些机制时恢复可解释社区的能力。在本文中，我们提出了AttDeCoDe，一种基于属性的密度社区检测框架的扩展，旨在分析节点特征在群体形成中起核心作用的网络。与仅基于网络拓扑定义密度不同，AttDeCoDe在属性空间中估计节点密度，使社区围绕基于属性的社区代表形成，同时保持结构性连接约束。这种方法自然捕捉由同质性驱动的聚集，同时对领导者影响保持敏感。我们通过基于新颖生成模型的模拟研究评估了所提出的方法，该模型扩展了度校正随机块模型，通过引入属性驱动的领导者吸引力来反映协作研究网络的关键特征。我们对来自地平线计划的研究合作数据进行了实证应用，其中组织由项目层面的主题描述符进行表征。结果表明，AttDeCoDe为属性网络中的社区检测提供了一种灵活且可解释的框架，其性能与基于拓扑和属性辅助的基准相媲美。
英文摘要：
Community structure in social and collaborative networks often emerges from a complex interplay between structural mechanisms, such as degree heterogeneity and leader-driven attraction, and homophily on node attributes. Existing community detection methods typically focus on these dimensions in isolation, limiting their ability to recover interpretable communities in presence of such mechanisms. In this paper, we propose AttDeCoDe, an attribute-driven extension of a density-based community detection framework, developed to analyse networks where node characteristics play a central role in group formation. Instead of defining density purely from network topology, AttDeCoDe estimates node-wise density in the attribute space, allowing communities to form around attribute-based community representatives while preserving structural connectivity constraints. This approach naturally captures homophily-driven aggregation while remaining sensitive to leader influence. We evaluate the proposed method through a simulation study based on a novel generative model that extends the degree-corrected stochastic block model by incorporating attribute-driven leader attraction, reflecting key features of collaborative research networks. We perform an empirical application to research collaboration data from the Horizon programmes, where organisations are characterised by project-level thematic descriptors. Both results show that AttDeCoDe offers a flexible and interpretable framework for community detection in attributed networks achieving competitive performance relative to topology-based and attribute-assisted benchmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
