Robust Training of Temporal GNNs using Nearest Neighbours based Hard Negatives
This work addresses a specific bottleneck in training TGNNs for future-link prediction, offering an incremental improvement over existing methods.
The paper tackled the problem of sub-optimal performance in temporal graph neural networks (TGNNs) due to uninformative negative sampling during training, and proposed an importance-based negative sampling method that achieved consistent superior performance across three real-world datasets.
Temporal graph neural networks Tgnn have exhibited state-of-art performance in future-link prediction tasks. Training of these TGNNs is enumerated by uniform random sampling based unsupervised loss. During training, in the context of a positive example, the loss is computed over uninformative negatives, which introduces redundancy and sub-optimal performance. In this paper, we propose modified unsupervised learning of Tgnn, by replacing the uniform negative sampling with importance-based negative sampling. We theoretically motivate and define the dynamically computed distribution for a sampling of negative examples. Finally, using empirical evaluations over three real-world datasets, we show that Tgnn trained using loss based on proposed negative sampling provides consistent superior performance.