Network analysis of conferences: Mapping the backbone of ESANN topics

Academic conferences are central to knowledge dissemination and community formation. The maps of science approach [Boyack, Klavans and Börner 2005] introduced the framework of visualization of sets of text documents, based on distances calculated from (dis)similarity between the text documents. Can a similar framework be applied to conferences, to visualize subtopics and their fine structure and time development? Here, we focus on the abstracts of the ESANN conference series to trace topic and collaboration dynamics from 2010 to 2025, based on Topic Modelling of ~2,000 abstracts used SPECTER Embeddings, DBSCAN/HDBSCAN Clustering, UMAP Visualisation, and Temporal Drift Analysis. A co-authorship network of 3,500 authors and 7,500 ties was examined through Centrality Measures, Clustering Coefficients, Louvain Community Detection, and Largest Connected Component Analysis. Our findings reveal continuity in Data Mining and Graph Learning, rapid growth in Deep Learning for Natural Language Processing and Medical Imaging, and a decline of Feature Selection and Spectral Clustering. The collaboration network shows a fragmented core-periphery structure reliant on a few hubs and brokers, reflecting both continuity and disruption in Machine Learning research.