提出新方法L-Modularity，精准发现动态网络中节点进出社区的时刻。
Discovering Communities in Continuous-Time Temporal Networks by Optimizing L-Modularity
- 基于贪婪优化纵向模块度，无需时间离散化
- 能准确捕捉节点加入或离开社区的具体时间点
- 适合研究真实动态网络中的社区演化，如社交或通信数据
社区检测是网络分析中的基础问题，在多个领域有广泛应用。将社区检测拓展到具有精确时间精度的时序网络，需针对交互的时间特性设计专门方法。本文提出LAGO，一种通过贪婪优化纵向模块度（Longitudinal Modularity）来发现动态社区的新方法，该模块度是为连续时间网络设计的模块度变体。与依赖时间离散化或假设社区刚性演化的传统方法不同，LAGO能够精确刻画节点进入和退出社区的时刻。我们在合成基准和真实数据集上评估了LAGO，结果表明其能高效识别在时间和拓扑上均一致的动态社区。
原文摘要 · Abstract (English)
Community detection is a fundamental problem in network analysis, with many applications in various fields. Extending community detection to the temporal setting with exact temporal accuracy, as required by real-world dynamic data, necessitates methods specifically adapted to the temporal nature of interactions. We introduce LAGO, a novel method for uncovering dynamic communities by greedy optimization of Longitudinal Modularity, a specific adaptation of Modularity for continuous-time networks. Unlike prior approaches that rely on time discretization or assume rigid community evolution, LAGO captures the precise moments when nodes enter and exit communities. We evaluate LAGO on synthetic benchmarks and real-world datasets, demonstrating its ability to efficiently uncover temporally and topologically coherent communities.
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