In dynamic complex networks, entities interact and form network communities that evolve over time. Among the many static Community Detection (CD) solutions, the modularity-based Louvain, or Greedy Modularity Algorithm (GMA), is widely employed in real-world applications due to its intuitiveness and scalability. Nevertheless, addressing CD in dynamic graphs remains an open problem, since the evolution of the network connections may poison the identification of communities, which may be evolving at a slower pace. Hence, naively applying GMA to successive network snapshots may lead to temporal inconsistencies in the communities. Two evolutionary adaptations of GMA, sGMA and $\alpha$GMA, have been proposed to tackle this problem. Yet, evaluating the performance of these methods and understanding to which scenarios each one is better suited is challenging because of the lack of a comprehensive set of metrics and a consistent ground truth. To address these challenges, we propose (i) a benchmarking framework for evolutionary CD algorithms in dynamic networks and (ii) a generalised modularity-based approach (NeGMA). Our framework allows us to generate synthetic community-structured graphs and design evolving scenarios with nine basic graph transformations occurring at different rates. We evaluate performance through three metrics we define, i.e. Correctness, Delay, and Stability. Our findings reveal that $\alpha$GMA is well-suited for detecting intermittent transformations, but struggles with abrupt changes; sGMA achieves superior stability, but fails to detect emerging communities; and NeGMA appears a well-balanced solution, excelling in responsiveness and instantaneous transformations detection.

翻译：在动态复杂网络中，实体之间的交互形成随时间演化的网络社区。在众多静态社区检测（CD）解决方案中，基于模块度的Louvain算法（即贪婪模块度算法GMA）因其直观性和可扩展性而被广泛应用于实际场景。然而，动态图中的社区检测仍是一个未解决的问题：网络连接的演化可能会干扰以较慢速度演化的社区识别过程。因此，将GMA简单应用于连续网络快照可能导致社区的时间不一致性。为解决此问题，研究者提出了GMA的两种进化变体——sGMA和αGMA。但由于缺乏全面的评估指标和一致的基准真值，评估这些方法的性能并理解其适用场景仍具挑战性。针对这些挑战，我们提出：（i）面向动态网络中进化社区检测算法的基准测试框架；（ii）一种广义模块度方法（NeGMA）。该框架可生成具有社区结构的合成图，并设计包含九种不同速率基础图变换的演化场景。通过定义的三项指标（正确性、延迟性和稳定性）进行评估，我们发现：αGMA适合检测间歇性变换，但在突发变化中表现不佳；sGMA具有卓越的稳定性，却无法检测新兴社区；而NeGMA作为均衡解决方案，在响应能力和瞬时变换检测方面表现优异。