# Graph neural networks

`kaal:entity:graph-neural-networks`

**Status.** derived

This node is assembled mechanically from the 4 claims that carry the concept tag `graph-neural-networks`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself.

## Every claim under this term

4 claims across 1 works, 2024 to 2024.

**2024**

- [4855607-010](https://wulfkaal.github.io/claims/4855607-010) [failure/evidenced] *(failure mode)* -- GNN scalability on large real world graphs is a genuine trade off rather than an engineering gap: sampling methods lose influential neighbors while clustering methods lose structural patterns, so each remedy sacrifices part of the signal the model needs.
  > Scalability is a major concern for GNNs on large real-world graphs, as sampling methods may lose influential neighbors while clustering methods may lose structural patterns.
  Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607
- [4855607-011](https://wulfkaal.github.io/claims/4855607-011) [failure/evidenced] *(failure mode)* -- GNNs are vulnerable to adversarial attacks that target both node features and graph structure, and their lack of interpretability remains a major obstacle to applying them to real world problems.
  > GNNs can be vulnerable to adversarial attacks on both node features and graph structure, and interpretability remains a major obstacle for applying GNNs to real-world problems.
  Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607
- [4855607-012](https://wulfkaal.github.io/claims/4855607-012) [failure/evidenced] *(failure mode)* -- Most GNN architectures assume homogeneous graph structures, so adapting them to heterogeneous graphs with diverse node and edge types remains an unsolved research challenge, and full batch training on large graphs suffers memory overflow.
  > Training GNNs on large graphs can be resource-intensive, and full-batch training methods suffer from memory overflow issues. Many GNNs assume homogeneous graph structures, and adapting these models to heterogeneous graphs with diverse node and edge types remains a significant research challenge.
  Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607
- [4855607-032](https://wulfkaal.github.io/claims/4855607-032) [mechanism/asserted] -- GNNs and web3 systems optimize each other through mutual feedback effects, and it is this bidirectional learning, rather than one system merely serving the other, that produces an evolutionary dynamic optimization process.
  > The integration and optimization of AI models such as GNNs and web3 systems happens in feedback effects between the two systems. Through these feedback effects both systems learn constantly from and with each other which results in an evolutionary dynamic optimization process.
  Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

## Verify

Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust.

    curl -s https://wulfkaal.github.io/entities/graph-neural-networks.md | sha256sum

**Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.