# GNN图神经网络

* [返回顶层目录](https://luweikxy.gitbook.io/machine-learning-notes/pages/-LpO5vE88qYwjk5WM_Qf#目录)
* [图神经网络综述](/machine-learning-notes/graph-neural-networks/graph-neural-networks-review.md)
* [图卷积网络](/machine-learning-notes/graph-neural-networks/graph-convolutional-networks.md)
  * [GCN图卷积网络初步理解](broken://pages/-LpO5vHFI1dTGcyZJtVa)
  * [GCN图卷积网络的numpy简单实现](broken://pages/-LpO5vHGnsIKDlOxlmmQ)
  * [GCN图卷积网络本质理解](/machine-learning-notes/graph-neural-networks/graph-convolutional-networks/gcn-essential-understand.md)
  * [GCN图卷积网络全面理解](/machine-learning-notes/graph-neural-networks/graph-convolutional-networks/gcn-comprehensive-understand.md)
  * [SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS ICLR2017](/machine-learning-notes/graph-neural-networks/graph-convolutional-networks/semi-supervised-classification-with-graph-convolutional-networks.md)
  * [Graph Convolutional Neural Networks for Web-Scale Recommender Systems KDD2018](/machine-learning-notes/advanced-knowledge/graph-convolutional-network/graph-convolutional-neural-networks-for-web-scale-recommender-systems.md)
* 图注意力网络


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://luweikxy.gitbook.io/machine-learning-notes/graph-neural-networks.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
