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The library\u2019s introduction is outlined through its code structure and primary features. The GNN framework adopts a graph bisection methodology that capitalizes on connectivity and geometric mesh information via SAGE convolutional layers, in line with the methodology proposed in (Antonietti and Manuzzi in J Comput Phys 452:110900, 2022; Antonietti et al. in Polytopal mesh agglomeration via geometrical deep learning for three-dimensional heterogeneous domains,\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/arxiv.org\/abs\/2406.10587\" ext-link-type=\"uri\">arXiv:2406.10587<\/jats:ext-link>\n                    , 2024). Additionally, the proposed  library incorporates reinforcement learning to enhance the accuracy and robustness of the model initially suggested in [1, 2] for predicting coarse partitions within a multilevel framework. A detailed tutorial is provided to guide the user through the process of mesh agglomeration and the training of a GNN bisection model. We present several examples of mesh agglomeration conducted by , demonstrating the library\u2019s applicability across various scenarios. Furthermore, the performance of the newly introduced models is contrasted with that of METIS and k-means, illustrating that the proposed GNN models are competitive regarding partition quality and computational efficiency. Finally, we exhibit the versatility of  \u2019s interface through its integration with , an open-source library implementing discontinuous Galerkin methods on polytopal grids for the numerical discretization of multiphysics differential problems.\n                  <\/jats:p>","DOI":"10.1007\/s00366-025-02223-y","type":"journal-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T08:36:15Z","timestamp":1761554175000},"page":"4825-4850","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MAGNET: an open-source library for mesh agglomeration by graph neural networks"],"prefix":"10.1007","volume":"41","author":[{"given":"Paola F.","family":"Antonietti","sequence":"first","affiliation":[]},{"given":"Matteo","family":"Caldana","sequence":"additional","affiliation":[]},{"given":"Ilario","family":"Mazzieri","sequence":"additional","affiliation":[]},{"given":"Andrea Re","family":"Fraschini","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"2223_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2021.110900","volume":"452","author":"PF Antonietti","year":"2022","unstructured":"Antonietti PF, Manuzzi E (2022) Refinement of polygonal grids using convolutional neural networks with applications to polygonal discontinuous galerkin and virtual element methods. 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