FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks
This addresses a key bottleneck in dynamic graph analysis for applications like social networks or recommendation systems, offering a novel solution to enhance efficiency and accuracy.
The paper tackles the problem of inefficient and static historical neighbor sampling in temporal graph neural networks (TGNNs) for future link prediction, introducing FLASH, a learnable adaptive mechanism that consistently improves TGNN performance across multiple benchmarks.
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 sampling or recent neighbors selection. These heuristics are static and fail to adapt to the underlying graph structure. We introduce FLASH, a learnable and graph-adaptive neighborhood selection mechanism that generalizes existing heuristics. FLASH integrates seamlessly into TGNNs and is trained end-to-end using a self-supervised ranking loss. We provide theoretical evidence that commonly used heuristics hinders TGNNs performance, motivating our design. Extensive experiments across multiple benchmarks demonstrate consistent and significant performance improvements for TGNNs equipped with FLASH.