{
  "id": "2405.15473",
  "version": "v1",
  "published": "2024-05-24T11:51:08.000Z",
  "updated": "2024-05-24T11:51:08.000Z",
  "title": "Encoder Embedding for General Graph and Node Classification",
  "authors": [
    "Cencheng Shen"
  ],
  "comment": "26 pages",
  "categories": [
    "stat.ML",
    "cs.LG",
    "cs.SI"
  ],
  "abstract": "Graph encoder embedding, a recent technique for graph data, offers speed and scalability in producing vertex-level representations from binary graphs. In this paper, we extend the applicability of this method to a general graph model, which includes weighted graphs, distance matrices, and kernel matrices. We prove that the encoder embedding satisfies the law of large numbers and the central limit theorem on a per-observation basis. Under certain condition, it achieves asymptotic normality on a per-class basis, enabling optimal classification through discriminant analysis. These theoretical findings are validated through a series of experiments involving weighted graphs, as well as text and image data transformed into general graph representations using appropriate distance metrics.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-05-24T11:51:08.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "encoder embedding",
      "node classification",
      "appropriate distance metrics",
      "general graph model",
      "achieves asymptotic normality"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 26,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}