Unsupervised Detection of Persistent Communities in Dynamic Networks with Network Embeddings - Le Coz, Ferdinand; Cabanac, Guillaume; Figeac, Julien (Proceedings of the International AAAI Conference on Web and Social Media, 2026)
Many real-world phenomena can be modeled using dynamicnetworks organized according to an evolving communitystructure. While traditional community discovery algorithmsrely on structural information, recent years have seen a shiftin focus towards methods of embedding nodes in dynamicgraphs that incorporate temporal information through times-tamps and contextual information through features. This en-deavour is particularly evident in Graph Neural Networks(GNNs). In this paper, we address the importance of nodeembeddings and features in understanding network dynamics.First, we propose a general framework for extracting dynamiccommunities from graph embeddings without prior knowl-edge of these communities. We then design a synthetic dy-namic graph generator that produces network data and fea-tures for GNN training. Experiments on synthetic dynamicnetworks demonstrate that informative features are essentialfor ensuring the performance of GNNs. By comparing severalembedding algorithms on different scenarios, we demonstratethat our method effectively captures the network’s underlyingdynamics at both the macro and node levels. Furthermore, wedemonstrate that GNN-based embeddings are more effectiveat capturing global network dynamics. They significantly out-perform conventional embedding or community detection al-gorithms when it comes to detecting changes in dynamics atthe node level. Additionally, we tested our method on a real-world dataset of social media interactions, achieving promis-ing results for network visualization.