{
  "access": "open",
  "cite_as": ["doi:10.21105/joss.04971"],
  "creators": [
    {
      "kind": "collaboration",
      "name": "GraphNeT team"
    }
  ],
  "curation": {
    "added": "2026-10-23",
    "source": "manual",
    "status": "curated"
  },
  "date_published": "2021-04-12",
  "date_updated": "2026-10-18",
  "description": "Python library for deep-learning-based reconstruction and classification of events in neutrino telescopes\n(IceCube, KM3NeT, P-ONE, …): data converters, graph representations of detector hits, GNN models and training\npipelines, with pretrained models.",
  "distributions": [
    {
      "formats": ["source-code"],
      "host": "github",
      "role": "primary",
      "url": "https://github.com/graphnet-team/graphnet"
    }
  ],
  "experiments": ["icecube", "km3net"],
  "id": "graphnet",
  "license": "Apache-2.0",
  "owner": {
    "kind": "organization",
    "name": "graphnet-team"
  },
  "references": [
    {
      "arxiv": "2210.12194",
      "citation": "GraphNeT, J. Open Source Softw. 8, 4971 (2023)",
      "doi": "10.21105/joss.04971",
      "relation": "describes"
    },
    {
      "arxiv": "2501.03817",
      "citation": "GraphNeT 2.0",
      "relation": "describes"
    }
  ],
  "short": "GraphNeT: deep-learning (graph neural network) library for event reconstruction in neutrino telescopes",
  "software": {
    "dependencies": ["PyTorch", "PyTorch Geometric"],
    "development_status": "active",
    "interfaces": ["library"],
    "languages": ["Python"],
    "purposes": ["reconstruction"],
    "repository": "https://github.com/graphnet-team/graphnet"
  },
  "sources": ["atmospheric", "astrophysical"],
  "title": "GraphNeT",
  "topics": ["detector-response"],
  "type": "software",
  "url_landing": "https://graphnet-team.github.io/graphnet/"
}