2026/02/15 by Nafiseh Sadat Sajadi, Behnam Bahrak, Mahdi Jafari Siavoshani
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Artificial neural network #Complex Network Analysis Techniques #Graph #Graph Theory and Algorithms #Graph theory #Link (geometry) #Topology (electrical circuits)
paper · pdf · open access · doi:10.1140/epjds/s13688-026-00670-1
published in EPJ Data Science 15(1) (Springer Nature)
openalex publication_date 2026/05/26 · openalex created_date 2026/05/27 · openalex updated_date 2026/08/05
Predicting links in sparse, continuously evolving networks is a central challenge in network science. Conventional heuristic methods and deep learning models, including Graph Neural Networks (GNNs), are typically designed for static graphs and thus struggle to capture temporal dependencies. Snapshot-based techniques partially address this issue but often encounter data sparsity and class imbalance, particularly in networks with transient interactions such as telecommunication call detail records (CDRs). Temporal Graph Networks (TGNs) model dynamic graphs by updating node embeddings over time; however, their predictive accuracy under sparse conditions remains limited. In this study, we improve the TGN framework by extracting enclosing subgraphs around candidate links, enabling the model to jointly learn structural and temporal information. Experiments on a sparse CDR, email, message dataset show that our approach increases average precision by at least 2% over standard TGNs, demonstrating the advantages of integrating local topology for robust link prediction in dynamic networks.