Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance

Dominik Fuchsgruber, Tim Postuvan, Stephan Günnemann, Simon Geisler

Keywords: Graph Neural Network, Graph, Edge, Equivariance, Invariance, Topology, Directed Graphs

TL;DR: We study edge-level problems with directed and undirected inputs and targets, and develop a GNN that satisfies novel theoretical desiderata.

Abstract:

Many applications in traffic, civil engineering, or electrical engineering revolve around edge-level signals. Such signals can be categorized as inherently directed, for example, the water flow in a pipe network, and undirected, like the diameter of a pipe. Topological methods model edge signals with inherent direction by representing them relative to a so-called *orientation* assigned to each edge.
They can neither model undirected edge signals nor distinguish if an edge itself is directed or undirected. We address these shortcomings by (i) revising the notion of *orientation equivariance* to enable edge direction-aware topological models, (ii) proposing *orientation invariance* as an additional requirement to describe signals without inherent direction, and (iii) developing EIGN, an architecture composed of novel direction-aware edge-level graph shift operators, that provably fulfils the aforementioned desiderata. It is the first work that discusses modeling directed and undirected signals while distinguishing between directed and undirected edges. A comprehensive evaluation shows that EIGN outperforms prior work in edge-level tasks, improving in RMSE on flow simulation tasks by up to 23.5%.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 7471