AI 中文总结
研究提出一种生成模型，通过基于互信息的遗传搜索控制社区演化与动态节点集，能处理节点增减，用概率生成时间边确保连通性，经实验验证可模拟真实社区演化，还用作基准研究节点变化对动态社区检测算法性能的影响。
AI 中文摘要
本文介绍了一种用于时间网络的生成模型，该模型联合控制社区演化和动态节点集。该模型将社区结构表示为一系列划分，并使用基于互信息的相似性度量引导的遗传搜索来调节快照之间的变化，从而在处理节点添加和删除时能够明确控制社区演化，包括分裂和合并。然后使用从数据或理论边界导出的社区内和社区间概率生成时间边以确保连通性。在真实世界数据集上的模拟实验证明了该生成模型对真实动态社区演化进行建模的能力。该模型被用作基准来研究节点加入/离开网络的速率对动态社区检测算法性能的影响。
英文摘要
This paper introduces a generative model for temporal networks that jointly controls community evolution and dynamic node sets. The model represents community structure as a sequence of partitions and uses a genetic search guided by a similarity measure based on mutual information to regulate changes between snapshots. This allows explicit control of community evolution including splits and merges while handling node additions and removals. Temporal edges are then generated using intra- and inter-community probabilities derived from data or theoretical bounds to ensure connectivity. Simulation experiments on real-world datasets demonstrate the ability of the generative model to model the evolution of real dynamic communities. The model is used as a benchmark to study the impact of the rate at which nodes join/leave the network on the performance of dynamic community detection algorithms.
