Transfer Learning for Temporal Link Prediction
Published in 2025 International Joint Conference on Neural Networks (IJCNN), 2025
Chatterjee, A., Ikica, B., Ravandi, B., & Palowitch, J. (2025). Transfer Learning for Temporal Link Prediction. 2025 International Joint Conference on Neural Networks (IJCNN), 1-8.Temporal graphs—networks that evolve over time—present challenges for link prediction models, which must generalize from one time window to future unknown connections. This paper investigates transfer learning for temporal link prediction, leveraging models pre-trained on diverse source graphs to improve performance on new target graphs with limited training data. We show that cross-graph transfer significantly boosts predictive accuracy and robustness over conventional baselines, with particular gains in sparse historical data settings.