Graph Automorphism Group Equivariant Neural Networks
Authors: Edward Pearce-Crump, William Knottenbelt
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper presents work that is primarily a theoretical contribution; hence we do not expect profound societal impact in the short term. However, in the medium term, a number of applications may well emerge from the theory having high levels of impact. We are the first to show how the combinatorics underlying bilabelled graphs provides the theoretical background for constructing neural networks that are equivariant to the automorphism group of a graph having n vertices where the layers are some tensor power of Rn. |
| Researcher Affiliation | Academia | Edward Pearce Crump 1 William J. Knottenbelt 1 1Department of Computing, Imperial College London, United Kingdom. Correspondence to: Edward Pearce Crump <ep1011@ic.ac.uk>. |
| Pseudocode | Yes | Procedure: Weight Matrix for an Aut(G)-Equivariant Linear Layer Function from (Rn) k to (Rn) l. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve experimental evaluation on datasets, thus no dataset availability information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation, thus no training/validation/test splits are specified. |
| Hardware Specification | No | The paper is theoretical and does not report specific experimental hardware used for its analysis or derivations. |
| Software Dependencies | No | The paper is theoretical and does not describe software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and describes a mathematical procedure rather than an experimental setup with hyperparameters or system-level training settings. |