A Hybrid TGN-SEAL Model for Dynamic Graph Link Prediction

• URL: http://arxiv.org/abs/2602.14239v1
• Date: Sun, 15 Feb 2026 17:16:47 GMT
• Title: A Hybrid TGN-SEAL Model for Dynamic Graph Link Prediction
• Authors: Nafiseh Sadat Sajadi, Behnam Bahrak, Mahdi Jafari Siavoshani,
• Abstract summary: Predicting links in sparse, continuously evolving networks is a central challenge in network science.<n>In this study, we improve theTemporal Graph Networks framework by extracting enclosing subgraphs around candidate links.<n> Experiments on a sparse CDR dataset show that our approach increases average precision by 2.6% over standard TGNs.
• Score: 0.5836991649815993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 dataset show that our approach increases average precision by 2.6% over standard TGNs, demonstrating the advantages of integrating local topology for robust link prediction in dynamic networks.

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