课程介绍

课程主页：Stanford CS224W · Machine Learning with Graphs

CS224w是顶级院校斯坦福出品的图机器学习方向专业课程，核心内容覆盖图数据挖掘、Node embedding、PageRank、标签传播、图神经网络、知识图谱、知识推理、子图挖掘、社区发现、生成模型等主题。对于graph方向的数据挖掘和机器学习(神经网络)有全面的知识覆盖和很高的权威度。如果大家想学习非结构化的图数据上的各类算法，本课程是最适合的课程之一。

课程主题与大纲

• Introduction; Machine Learning for Graphs
• Traditional Methods for ML on Graphs
• Node Embeddings
• Link Analysis: PageRank
• Label Propagation for Node Classification
• Graph Neural Networks 1: GNN Model
• Graph Neural Networks 2: Design Space
• Applications of Graph Neural Networks
• Theory of Graph Neural Networks
• Knowledge Graph Embeddings
• Reasoning over Knowledge Graphs
• Frequent Subgraph Mining with GNNs
• Community Structure in Networks
• Traditional Generative Models for Graphs
• Deep Generative Models for Graphs
• Advanced Topics on GNNs
• Scaling Up GNNs
• Guest Lecture: GNNs for Computational Biology
• Guest Lecture: Industrial Applications of GNNs
• GNNs for Science

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