Boosting Graph Neural Networks by Injecting Pooling in Message Passing

TL;DR

This paper introduces a novel message-passing framework for graph neural networks inspired by image processing filters, which effectively prevents over-smoothing and improves performance on benchmark datasets.

Contribution

The proposed bilateral-MP framework generalizes all ordinary MP GNNs and incorporates class-aware pairwise gradients to preserve graph structure and mitigate over-smoothing.

Findings

Improves GNN performance on five benchmark datasets.

Effectively alleviates over-smoothing in GNNs.

Validated by quantitative measurements.

Abstract

There has been tremendous success in the field of graph neural networks (GNNs) as a result of the development of the message-passing (MP) layer, which updates the representation of a node by combining it with its neighbors to address variable-size and unordered graphs. Despite the fruitful progress of MP GNNs, their performance can suffer from over-smoothing, when node representations become too similar and even indistinguishable from one another. Furthermore, it has been reported that intrinsic graph structures are smoothed out as the GNN layer increases. Inspired by the edge-preserving bilateral filters used in image processing, we propose a new, adaptable, and powerful MP framework to prevent over-smoothing. Our bilateral-MP estimates a pairwise modular gradient by utilizing the class information of nodes, and further preserves the global graph structure by using the gradient when the aggregating function is applied. Our proposed scheme can be generalized to all ordinary MP GNNs. Experiments on five medium-size benchmark datasets using four state-of-the-art MP GNNs indicate that the bilateral-MP improves performance by alleviating over-smoothing. By inspecting quantitative measurements, we additionally validate the effectiveness of the proposed mechanism in preventing the over-smoothing issue.