Robust Training of Temporal GNNs using Nearest Neighbours based Hard Negatives

• URL: http://arxiv.org/abs/2402.09239v1
• Date: Wed, 14 Feb 2024 15:27:53 GMT
• Title: Robust Training of Temporal GNNs using Nearest Neighbours based Hard Negatives
• Authors: Shubham Gupta, Srikanta Bedathur
• Abstract summary: Training of temporal graph neural networks Tgnn is enumerated by random sampling based unsupervised loss. We propose modified unsupervised learning of Tgnn, by replacing the uniform negative sampling with importance-based negative sampling. We show that Tgnn trained using loss based on proposed negative sampling provides consistent superior performance.
• Score: 44.49975766084011
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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