Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917946783170560 |
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| author | Fuchsgruber, Dominik Poštuvan, Tim Günnemann, Stephan Geisler, Simon |
| author_facet | Fuchsgruber, Dominik Poštuvan, Tim Günnemann, Stephan Geisler, Simon |
| contents | 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. These approaches 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 fulfills the aforementioned desiderata. It is the first general-purpose topological GNN for edge-level signals that can model directed and undirected signals while distinguishing between directed and undirected edges. A comprehensive evaluation shows that EIGN outperforms prior work in edge-level tasks, for example, improving in RMSE on flow simulation tasks by up to 23.5%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16935 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance Fuchsgruber, Dominik Poštuvan, Tim Günnemann, Stephan Geisler, Simon Machine Learning 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. These approaches 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 fulfills the aforementioned desiderata. It is the first general-purpose topological GNN for edge-level signals that can model directed and undirected signals while distinguishing between directed and undirected edges. A comprehensive evaluation shows that EIGN outperforms prior work in edge-level tasks, for example, improving in RMSE on flow simulation tasks by up to 23.5%. |
| title | Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.16935 |