Temporal Graph Rewiring with Expander Graphs

Katarina Petrović, Shenyang Huang, Farimah Poursafaei, Petar Veličković

Track: Extended abstract

Keywords: Temporal Graphs, Dynamic Graphs, Graph Neural Network, Graph Representation Learning

TL;DR: First method for Temporal Graph Rewiring aimed at relieving bottlenecks, oversquashing and memory staleness in TGNNs.

Abstract: Evolving relations in real-world networks are often modelled by temporal graphs. Graph rewiring techniques have been utilised on Graph Neural Networks (GNNs) to improve expressiveness and increase model performance. In this work, we propose Temporal Graph Rewiring (TGR), the first approach for graph rewiring on temporal graphs. TGR enables communication between temporally distant nodes in a continuous time dynamic graph by utilising expander graph propagation to construct a message passing highway for message passing between distant nodes. Expander graphs are suitable candidates for rewiring as they help overcome the oversquashing problem often observed in GNNs. On the public tgbl-wiki benchmark, we show that TGR improves the performance of a widely used TGN model by a significant margin, our code repository is accessible at: https://github.com/kpetrovicc/TGR.

Submission Number: 19