{
  "id": "2211.14555",
  "version": "v1",
  "published": "2022-11-26T12:54:45.000Z",
  "updated": "2022-11-26T12:54:45.000Z",
  "title": "Distribution Free Prediction Sets for Node Classification",
  "authors": [
    "Jase Clarkson"
  ],
  "comment": "To appear as an extended abstract at the LoG 2022 conference",
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "Graph Neural Networks (GNNs) are able to achieve high classification accuracy on many large real world datasets, but provide no rigorous notion of predictive uncertainty. We leverage recent advances in conformal prediction to construct prediction sets for node classification in inductive learning scenarios, and verify the efficacy of our approach across standard benchmark datasets using popular GNN models. The code is available at \\href{https://github.com/jase-clarkson/graph_cp}{this link}.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-11-26T12:54:45.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "distribution free prediction sets",
      "node classification",
      "large real world datasets",
      "achieve high classification accuracy",
      "standard benchmark datasets"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}