Robust Training of Temporal GNNs using Nearest Neighbours based Hard Negatives
<|reference_start|>Robust Training of Temporal GNNs using Nearest Neighbours based Hard Negatives: 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.<|reference_end|>
@article{gupta2024robust,
    title={Robust Training of Temporal GNNs using Nearest Neighbours based Hard
  Negatives},
    author={Shubham Gupta, Srikanta Bedathur},
    journal={arXiv preprint arXiv:2402.09239},
    year={2024},
    doi={10.1145/3632410.3632464},
    archivePrefix={arXiv},
    eprint={2402.09239},
    primaryClass={cs.LG cs.IR}
}