{
  "id": "1909.00958",
  "version": "v1",
  "published": "2019-09-03T05:21:31.000Z",
  "updated": "2019-09-03T05:21:31.000Z",
  "title": "Graph Representation Learning: A Survey",
  "authors": [
    "Fenxiao Chen",
    "Yuncheng Wang",
    "Bin Wang",
    "C. -C. Jay Kuo"
  ],
  "categories": [
    "cs.LG",
    "cs.SI",
    "stat.ML"
  ],
  "abstract": "Research on graph representation learning has received a lot of attention in recent years since many data in real-world applications come in form of graphs. High-dimensional graph data are often in irregular form, which makes them more difficult to analyze than image/video/audio data defined on regular lattices. Various graph embedding techniques have been developed to convert the raw graph data into a low-dimensional vector representation while preserving the intrinsic graph properties. In this review, we first explain the graph embedding task and its challenges. Next, we review a wide range of graph embedding techniques with insights. Then, we evaluate several state-of-the-art methods against small and large datasets and compare their performance. Finally, potential applications and future directions are presented.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-09-03T05:21:31.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "graph representation learning",
      "graph embedding techniques",
      "low-dimensional vector representation",
      "raw graph data",
      "high-dimensional graph data"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}