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Bottlenecks within Graph Neural networks: A Study of Challenges and Mitigations

This project will explore the oversquashing problem in Graph Neural Networks (GNNs) and how it affects the ability for the model to learn long-range dependencies between nodes. The project will also explore the use of topological information in the form of the dataset's graph structure to improve the model's ability to learn long-range dependencies.

Supervised by: Raghavendra Selvan