SEA: Graph Shell Attention in Graph Neural Networks

TL;DR

SEA introduces a novel graph neural network architecture that employs a routing heuristic to assign nodes to dedicated experts, each processing localized subgraphs, thereby enhancing expressiveness and addressing over-smoothing.

Contribution

The paper proposes Graph Shell Attention (SEA), a new GNN framework that routes node representations to experts based on subgraph views, improving expressiveness over traditional GNNs.

Findings

Competitive results on benchmark datasets

Enhanced expressiveness with increased experts

Addresses over-smoothing in deep GNNs

Abstract

A common issue in Graph Neural Networks (GNNs) is known as over-smoothing. By increasing the number of iterations within the message-passing of GNNs, the nodes' representations of the input graph align with each other and become indiscernible. Recently, it has been shown that increasing a model's complexity by integrating an attention mechanism yields more expressive architectures. This is majorly contributed to steering the nodes' representations only towards nodes that are more informative than others. Transformer models in combination with GNNs result in architectures including Graph Transformer Layers (GTL), where layers are entirely based on the attention operation. However, the calculation of a node's representation is still restricted to the computational working flow of a GNN. In our work, we relax the GNN architecture by means of implementing a routing heuristic. Specifically, the nodes' representations are routed to dedicated experts. Each expert calculates the representations according to their respective GNN workflow. The definitions of distinguishable GNNs result from k-localized views starting from the central node. We call this procedure Graph Shell Attention (SEA), where experts process different subgraphs in a transformer-motivated fashion. Intuitively, by increasing the number of experts, the models gain in expressiveness such that a node's representation is solely based on nodes that are located within the receptive field of an expert. We evaluate our architecture on various benchmark datasets showing competitive results compared to state-of-the-art models.