Enhancing Hyperbolic Graph Embeddings via Contrastive Learning. (arXiv:2201.08554v1 [cs.LG])

Web: http://arxiv.org/abs/2201.08554

Recently, hyperbolic space has risen as a promising alternative for
semi-supervised graph representation learning. Many efforts have been made to
design hyperbolic versions of neural network operations. However, the inspiring
geometric properties of this unique geometry have not been fully explored yet.
The potency of graph models powered by the hyperbolic space is still largely
underestimated. Besides, the rich information carried by abundant unlabelled
samples is also not well utilized. Inspired by the recently active and emerging
self-supervised learning, in …

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