| license: mit | |
| pipeline_tag: graph-ml | |
| # GNN-based Multigraph Solver - GMS | |
| Model weights and training/test files for the ICLR2026 paper [Beyond Simple Graphs: Neural Multi-Objective Routing on Multigraphs](https://openreview.net/forum?id=55laGcPNZZ). | |
| In short, we extend end-to-end neural vehicle routing to problems defined on multigraphs. | |
| Code and usage instructions can be found in the project [Github repository](https://github.com/filiprydin/GMS). | |
| ## Reference | |
| If you find our work useful, please consider citing our paper: | |
| ``` | |
| @inproceedings{rydin2026beyond, | |
| title={Beyond Simple Graphs: Neural Multi-Objective Routing on Multigraphs}, | |
| author={Filip Rydin and Attila Lischka and Jiaming Wu and Morteza Haghir Chehreghani and Balázs Kulcsár}, | |
| booktitle={International Conference on Learning Representations}, | |
| year={2026}, | |
| url={https://openreview.net/forum?id=55laGcPNZZ} | |
| } | |
| ``` |