arXiv AI By Sanyam Sanjay Jain, Anshika Krishnatray, Aditya Sharma, Vinti Agarwal

Repurposing Unified Topological Signatures for Graph Representation Learning

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arXiv Machine Learning
Aug 19

HyPE-GT: where Graph Transformers meet Hyperbolic Positional Encodings

HyPE-GT introduces a framework that generates learnable hyperbolic positional encodings for Graph Transformers, enabling the capture of complex hierarchical relationships in graph-structured data. Unlike traditional Euclidean encodings, HyPE’s hyperbolic encodings can be selected to suit specific downstream tasks and help mitigate oversmoothing in deep Graph Neural Networks. Experiments on molecular benchmarks and large-scale Open Graph Benchmark datasets demonstrate improved performance, while additional tests on Coauthor and Copurchase networks confirm HyPE’s effectiveness in controlling oversmoothing.

By Kushal Bose, Swagatam Das