Towards Deeper Graph Neural Networks with Differentiable Group Normalization

TL;DR

This paper introduces differentiable group normalization (DGN), a novel technique to mitigate over-smoothing in deep graph neural networks by normalizing nodes within groups, leading to improved robustness and performance.

Contribution

The paper proposes a new differentiable group normalization method that effectively alleviates over-smoothing in deep GNNs by considering community structures.

Findings

DGN improves GNN robustness to over-smoothing.

Deeper GNNs with DGN outperform existing models.

Enhanced performance on real-world datasets.

Abstract

Graph neural networks (GNNs), which learn the representation of a node by aggregating its neighbors, have become an effective computational tool in downstream applications. Over-smoothing is one of the key issues which limit the performance of GNNs as the number of layers increases. It is because the stacked aggregators would make node representations converge to indistinguishable vectors. Several attempts have been made to tackle the issue by bringing linked node pairs close and unlinked pairs distinct. However, they often ignore the intrinsic community structures and would result in sub-optimal performance. The representations of nodes within the same community/class need be similar to facilitate the classification, while different classes are expected to be separated in embedding space. To bridge the gap, we introduce two over-smoothing metrics and a novel technique, i.e., differentiable group normalization (DGN). It normalizes nodes within the same group independently to increase their smoothness, and separates node distributions among different groups to significantly alleviate the over-smoothing issue. Experiments on real-world datasets demonstrate that DGN makes GNN models more robust to over-smoothing and achieves better performance with deeper GNNs.