Author

Kushal Bose

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#machine learning Sep 2025

Asynchronous Message Passing for Addressing Oversquashing in Graph Neural Networks

This work proposes an efficient, model-agnostic framework that asynchronously updates node features across layers, unlike standard synchronous message passing, and shows theoretically that the framework's sensitivity bound decays more slowly with depth than synchronous message passing.

Kushal Bose, Swagatam Das · arXiv.org · 0 citations

#machine learning Dec 2023

HyPE-GT: where Graph Transformers meet Hyperbolic Positional Encodings

Comprehensive experiments on four molecular benchmarks, including the four large-scale Open Graph Benchmark datasets, substantiate the effectiveness of hyperbolic positional encodings in enhancing the performance of Graph Transformers and provide extensive theoretical underpinnings to offer insights into the working mechanism of the HyPE framework.

Kushal Bose, Swagatam Das · arXiv.org · 2 citations