  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.
