arXiv cs.LG
· Papers
FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks
arXiv:2504.07337v2 Announce Type: replace Abstract: 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 he