Home | Publications | FMS+22

SEA: Graph Shell Attention in Graph Neural Networks

MCML Authors

Christian Frey

Dr.

* Former Member

→ Group Matthias Schubert
Spatial Artificial Intelligence

Yunpu Ma

Dr.

→ Group Volker Tresp
Database Systems, Data Mining and AI

Matthias Schubert

Prof. Dr.

Principal Investigator

Spatial Artificial Intelligence

Abstract

A common problem 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 and become indiscernible. The latest models employing attention mechanisms with Graph Transformer Layers (GTLs) are still restricted to the layer-wise computational workflow of a GNN that are not beyond preventing such effects. 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 textbf{S}htextbf{e}ll textbf{A}ttention (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 while drastically reducing the number of parameters compared to state-of-the-art models.

inproceedings