{
  "@context": "https://schema.org",
  "@type": "TechArticle",
  "@id": "https://anchorfact.org/kb/graph-neural-networks",
  "headline": "Graph Neural Networks: Message Passing and Applications",
  "description": "Graph Neural Networks extend deep learning to graph-structured data — molecules, social networks, knowledge graphs. Message passing enables nodes to learn from their local neighborhood, creating representations that capture both structure and features.",
  "dateCreated": "2026-05-24T02:49:13.613Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "Semi-Supervised Classification with Graph Convolutional Networks (GCN)",
      "sameAs": "https://arxiv.org/abs/1609.02907"
    },
    {
      "@type": "CreativeWork",
      "name": "Graph Representation Learning (Hamilton)",
      "sameAs": "https://www.cs.mcgill.ca/~wlh/grl_book/"
    }
  ]
}