On the Bottleneck of Graph Neural Networks and its Practical Implications

TL;DR

This paper identifies the over-squashing bottleneck in GNNs that hampers long-range information propagation, and demonstrates that breaking this bottleneck improves GNN performance on tasks requiring long-distance message passing.

Contribution

The paper introduces the concept of over-squashing as a key bottleneck in GNNs and shows how breaking this bottleneck enhances their ability to handle long-range interactions.

Findings

Over-squashing limits GNNs' ability to propagate long-range information.

GAT and GGNN are less susceptible to over-squashing than GCN and GIN.

Breaking the bottleneck improves state-of-the-art results without additional tuning.

Abstract

Since the proposal of the graph neural network (GNN) by Gori et al. (2005) and Scarselli et al. (2008), one of the major problems in training GNNs was their struggle to propagate information between distant nodes in the graph. We propose a new explanation for this problem: GNNs are susceptible to a bottleneck when aggregating messages across a long path. This bottleneck causes the over-squashing of exponentially growing information into fixed-size vectors. As a result, GNNs fail to propagate messages originating from distant nodes and perform poorly when the prediction task depends on long-range interaction. In this paper, we highlight the inherent problem of over-squashing in GNNs: we demonstrate that the bottleneck hinders popular GNNs from fitting long-range signals in the training data; we further show that GNNs that absorb incoming edges equally, such as GCN and GIN, are more susceptible to over-squashing than GAT and GGNN; finally, we show that prior work, which extensively tuned GNN models of long-range problems, suffers from over-squashing, and that breaking the bottleneck improves their state-of-the-art results without any tuning or additional weights. Our code is available at https://github.com/tech-srl/bottleneck/ .