---
id: graph-neural-networks
title: "Graph Neural Networks: Message Passing and Applications"
schema_type: TechArticle
category: ai
language: en
confidence: high
last_verified: "2026-05-28"
created_date: "2026-05-24"
generation_method: ai_structured
ai_models:
  - claude-opus
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
atomic_facts:
  - id: fact-gnn-1
    statement: Graph convolutional networks learn node representations by propagating information over graph structure.
    source_title: Semi-Supervised Classification with Graph Convolutional Networks
    source_url: https://arxiv.org/abs/1609.02907
    confidence: medium
  - id: fact-gnn-2
    statement: Graph Attention Networks apply masked self-attention to weight neighboring nodes during message passing.
    source_title: Graph Attention Networks
    source_url: https://arxiv.org/abs/1710.10903
    confidence: medium
  - id: fact-gnn-3
    statement: >-
      Hamilton frames graph representation learning as learning embeddings and neural methods for
      graph-structured data.
    source_title: Graph Representation Learning
    source_url: https://www.cs.mcgill.ca/~wlh/grl_book/
    confidence: medium
completeness: 0.84
primary_sources:
  - title: Semi-Supervised Classification with Graph Convolutional Networks
    type: academic_paper
    year: 2017
    url: https://arxiv.org/abs/1609.02907
    institution: ICLR / arXiv
  - title: Graph Attention Networks
    type: academic_paper
    year: 2018
    url: https://arxiv.org/abs/1710.10903
    institution: ICLR / arXiv
  - title: Graph Representation Learning
    type: book
    year: 2020
    url: https://www.cs.mcgill.ca/~wlh/grl_book/
    institution: McGill University
    authors:
      - William L. Hamilton
known_gaps:
  - This compact repair keeps only source-mapped public claims from the sampled audit entry.
disputed_statements: []
secondary_sources: []
updated: "2026-05-28"
---

## TL;DR

Graph neural networks learn from relational structure such as nodes, edges, and neighborhoods. This repair avoids unsupported application claims and keeps the public facts to core methods.

## Core Explanation

The previous version mixed broad, duplicate, future, or mismatched evidence. The repaired entry keeps three public claims that map directly to the listed primary sources.

## Further Reading

- [Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/abs/1609.02907)
- [Graph Attention Networks](https://arxiv.org/abs/1710.10903)
- [Graph Representation Learning](https://www.cs.mcgill.ca/~wlh/grl_book/)