1. Robust Training of Temporal GNNs using Nearest Neighbours based Hard Negatives
- Author
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Gupta, Shubham and Bedathur, Srikanta
- Subjects
Computer Science - Machine Learning ,Computer Science - Information Retrieval - 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., Comment: 10 pages
- Published
- 2024
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