Or Feldman — accepted AI-conference papers

2026

FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks

Or Feldman, Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb, Chaim Baskin, Moshe Eliasof

IJCAI 2026

Aggregating temporal signals from historic interactions is a key step in future link prediction on dynamic graphs. However, incorporating long histories is resource-intensive. Hence, temporal graph neural networks (TGNNs) often rely on historical neighbors sampling heuristics such as uniform samplin

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2026

Revisting Node Affinity Prediction In Temporal Graphs

Krishna Sri Ipsit Mantri, Or Feldman, Moshe Eliasof, Chaim Baskin

ICLR 2026poster

Node affinity prediction is a common task that is widely used in temporal graph learning with applications in social and financial networks, recommender systems, and more. Recent works have addressed this task by adapting state-of-the-art dynamic link property prediction models to node affinity pre…

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