Graph Neural Networks and Arithmetic Circuits
Authors: Timon Barlag, Vivian Holzapfel, Laura Strieker, Jonni Virtema, Heribert Vollmer
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We characterize the computational power of neural networks that follow the graph neural network (GNN) architecture, not restricted to aggregate-combine GNNs or other particular types. We establish an exact correspondence between the expressivity of GNNs using diverse activation functions and arithmetic circuits over real numbers. |
| Researcher Affiliation | Academia | Timon Barlag Institute for Theoretical Computer Science Leibniz University Hanover Hanover, Germany Vivian Holzapfel Institute for Theoretical Computer Science Leibniz University Hanover Hanover, Germany Laura Strieker Institute for Theoretical Computer Science Leibniz University Hanover Hanover, Germany Jonni Virtema School of Computer Science University of Sheffield Sheffield, United Kingdom Heribert Vollmer Institute for Theoretical Computer Science Leibniz University Hanover Hanover, Germany |
| Pseudocode | No | The paper contains figures illustrating circuit examples, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper is purely theoretical and does not mention releasing any source code for its methodology. The NeurIPS checklist indicates 'NA' for questions regarding code and data. |
| Open Datasets | No | The paper is theoretical and does not involve datasets or training. The NeurIPS checklist indicates 'NA' for questions regarding experimental reproducibility. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for validation. The NeurIPS checklist indicates 'NA' for questions regarding experimental reproducibility. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments, thus no hardware specifications are mentioned. The NeurIPS checklist indicates 'NA' for questions regarding experimental resources. |
| Software Dependencies | No | The paper is theoretical and does not describe any software dependencies with specific version numbers. The NeurIPS checklist indicates 'NA' for questions regarding experimental resources. |
| Experiment Setup | No | The paper is theoretical and does not conduct experiments, thus no experimental setup details like hyperparameters are provided. The NeurIPS checklist indicates 'NA' for questions regarding experimental settings. |