---
# System prepended metadata

title: Graph Neural Networks
tags: [機器學習]

---

# Graph Neural Networks

###### tags: `機器學習`


因為網路上資源頗多，所以這邊就貼上看起來還不錯的學習資源當作參考。

reference: [YT 李弘毅 - GNN](https://www.youtube.com/watch?v=eybCCtNKwzA)

其他筆記: 
* [番外.李宏毅学习笔记.12.GNN](https://blog.csdn.net/oldmao_2001/article/details/105457201)
* [【李宏毅2020 ML/DL】P18-19 Graph Neural Network](https://blog.csdn.net/weixin_42815609/article/details/107577956)
* [閱讀筆記 : A Comprehensive Survey on Graph Neural Networks ( Spatial-based ConvGNNs 篇)](https://z8663z.medium.com/%E9%96%B1%E8%AE%80%E7%AD%86%E8%A8%98-a-comprehensive-survey-on-graph-neural-networks-spatial-based-convgnns-%E7%AF%87-3aaeca24f371)

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## 術語補充:
1. **Aggregation:** 使用某 node neighbor feature update 下一層的hidden state。
2. **Readout:** 將所有 node 的 feature 集合成一個輸出。
( 因為一個 graph 通常就是要輸出一個結果嘛！例如: 一個分子是甚麼種類。)

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## 作法參考: 
1. [NN4G ->Neural Network for Graphs: A Contextual Constructive Approach](https://ieeexplore.ieee.org/document/4773279)

    ![](https://i.imgur.com/yItypPb.png =80%x)

    ![](https://i.imgur.com/oxm3fhh.png =80%x)

2. DCNN（Dynamic Convolution Neural Network）
3. DGC (Dynamic Group Convolution)
4. MoNET (Mixture Model Network) - 事先定義好 weight
    ![](https://i.imgur.com/4lh5t8K.png =80%x)
5. GAT (Graph Attension Network) - 自己去學鄰居的 weighted sum，對鄰居做 attension
6. GIN (Graph isomorphism Network)
    ![](https://i.imgur.com/V6AX4bJ.png =80%x)


影片: https://www.youtube.com/watch?v=M9ht8vsVEw8
