CoDÆN: Benchmarks and Comparison of Evolutionary Community Detection Algorithms for Dynamic Networks

Web data are often modelled as complex networks in which entities interact and form communities. Nevertheless, web data evolves over time, and network communities change alongside it. This makes Community Detection (CD) in dynamic graphs a relevant problem, calling for evolutionary CD algorithms. The choice and evaluation of such algorithm performance is challenging because of the lack of a comprehensive set of benchmarks and specific metrics. To address these challenges, we propose CoDÆN—Community Detection Algorithms in Evolving Networks—a benchmarking framework for evolutionary CD algorithms in dynamic networks, that we offer as open source to the community. CoDÆN allows us to generate synthetic community-structured graphs with known ground truth and design evolving scenarios combining nine basic graph transformations that modify edges, nodes, and communities. We propose three complementary metrics (i.e., Correctness, Delay, and Stability) to compare evolutionary CD algorithms. Armed with CoDÆN, we consider three evolutionary modularity-based CD approaches, dissecting their performance to gauge the trade-off between the stability of the communities and their correctness. Next, we compare the algorithms in real Web-oriented datasets, confirming such a trade-off. Our findings reveal that algorithms that introduce memory in the graph maximise stability but add delay when abrupt changes occur. Conversely, algorithms that introduce memory by initialising the CD algorithms with the previous solution fail to identify the split and birth of new communities. These observations underscore the value of CoDÆN in facilitating the study and comparison of alternative evolutionary community detection algorithms.