A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks

• URL: http://arxiv.org/abs/2412.07243v1
• Date: Tue, 10 Dec 2024 07:07:06 GMT
• Title: A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks
• Authors: Biswadeep Chakraborty, Harshit Kumar, Saibal Mukhopadhyay,
• Abstract summary: Oversmoothing in Graph Neural Networks (GNNs) poses a significant challenge as network depth increases. We identify the root causes of oversmoothing and propose textbftextitDYNAMO-GAT. Our theoretical analysis reveals how DYNAMO-GAT disrupts the convergence to oversmoothed states.
• Score: 18.185834696177654
• License:
• Abstract: Oversmoothing in Graph Neural Networks (GNNs) poses a significant challenge as network depth increases, leading to homogenized node representations and a loss of expressiveness. In this work, we approach the oversmoothing problem from a dynamical systems perspective, providing a deeper understanding of the stability and convergence behavior of GNNs. Leveraging insights from dynamical systems theory, we identify the root causes of oversmoothing and propose \textbf{\textit{DYNAMO-GAT}}. This approach utilizes noise-driven covariance analysis and Anti-Hebbian principles to selectively prune redundant attention weights, dynamically adjusting the network's behavior to maintain node feature diversity and stability. Our theoretical analysis reveals how DYNAMO-GAT disrupts the convergence to oversmoothed states, while experimental results on benchmark datasets demonstrate its superior performance and efficiency compared to traditional and state-of-the-art methods. DYNAMO-GAT not only advances the theoretical understanding of oversmoothing through the lens of dynamical systems but also provides a practical and effective solution for improving the stability and expressiveness of deep GNNs.

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