Stanford CS224W: ML with Graph

学习进度

课程	学习进度
8. Applications of Graph Neural Networks	图增强 √，预测头 ×
9. Theory of Graph Neural Networks
10. Knowledge Graph Embeddings	√
11. Reasoning over Knowledge Graphs	看不懂啊
12. Frequent Subgraph Mining with GNNs	√
13. Community Structure in Networks	Louvain 算法 ×
14. Traditional Generative Models for Graphs
15. Deep Generative Models for Graphs
16. Advanced Topics on GNNs
17. Scaling Up GNNs
19. GNNs for Science

课程介绍

摘自官网。

Complex data can be represented as a graph of relationships between objects. Such networks are a fundamental tool for modeling social, technological, and biological systems. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools apt to reveal insights on a variety of networks. 复杂数据可以表示为对象之间关系的图形。这样的网络是建模社会、技术和生物系统的基本工具。本课程侧重于计算、算法和建模挑战。针对大规模图的分析。通过研究底层图结构及其特征，学生将介绍机器学习技术和数据挖掘工具，以揭示各种网络的见解。

参考资料

• CS224W | Home (stanford.edu)

• Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 1.1 - Why Graphs (youtube.com)

• Contents (snap-stanford.github.io)

• PolarisRisingWar/cs224w-2021-winter-colab: cs224w（图机器学习）2021 冬季课程的 colab (github.com)

• thunlp/GNNPapers: Must-read papers on graph neural networks (GNN) (github.com)