时间网络中的中心性度量：批判性与比较性综述
Centrality Measures in Temporal Networks: A Critical and Comparative Survey
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中文总结 AI 辅助
本文对时间网络中心性度量展开批判性比较综述，提出功能分类法，在真实数据集上通过多维度实验评估度量，为不同场景选度量提供见解并指出未来研究方向。
中文摘要 AI 辅助
时间网络为通信、交通和社交网络等交互随时间变化的复杂系统提供了合适的表示方式。在这类网络中识别有影响力的节点比在静态图中更具挑战性，因为节点重要性不仅取决于网络结构，还取决于交互的时间和顺序。尽管已提出许多时间中心性度量，但相关文献仍较为零散，对其比较性能和适用性的共识有限。本文对时间网络中的中心性度量进行了批判性和比较性综述。我们回顾了经典中心性度量的时间扩展以及专门为时间图设计的度量，并提出了一种功能分类法，该分类法根据时间网络中量化影响力的主要机制对现有方法进行分类，将时间中心性度量分为基于交互、基于路径、基于游走、基于谱和基于鲁棒性的类别，为其潜在原理提供了统一视角。此外，我们提供了比较见解，以帮助在不同网络特征和应用场景下选择合适的时间中心性度量。为补充该综述，我们在多个真实世界时间网络数据集上进行了实验，通过使用流行病扩散模型的影响力传播实验、基于Kendall秩相关的排名一致性分析以及运行时复杂度分析来评估度量，以评估计算效率和可扩展性。最后，我们强调了关键的开放挑战和未来研究方向，包括百万规模网络的可扩展性以及对标准化评估框架的需求。
英文摘要
Temporal networks offer a suitable representation for complex systems in which interactions vary over time, such as communication, transportation, and social networks. Identifying influential nodes in such networks is more challenging than in static graphs because node importance depends not only on network structure but also on the timing and ordering of interactions. Although many temporal centrality measures have been proposed, the literature remains fragmented, with limited consensus on their comparative performance and applicability. This paper presents a critical and comparative survey of centrality measures in temporal networks. We review both temporal extensions of classical centrality metrics and measures specifically designed for temporal graphs, and propose a functional taxonomy that categorizes existing approaches according to the primary mechanism through which influence is quantified in temporal networks. The proposed taxonomy organizes temporal centrality measures into interaction-based, path-based, walk-based, spectral-based and robustness-based categories, providing a unified perspective on their underlying principles. In addition, we provide comparative insights to help select appropriate temporal centrality measures under different network characteristics and application settings. To complement the survey, we conduct experiments on multiple real-world temporal network datasets. The measures are evaluated through influence spreading experiments using epidemic diffusion models, ranking consistency analysis based on Kendall's rank correlation, and runtime complexity analysis to assess computational efficiency and scalability. Finally, we highlight key open challenges and future research directions, including scalability for million-sized networks and the need for standardized evaluation frameworks.
发表机构
- National Institute of Technology Hazratbal（Hazratbal国立技术学院）
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