课程 | 2023年秋季斯坦福CS224W《图机器学习》

课程简介:

复杂的数据可以表示为对象之间的关系图。这种网络是对社会、技术和生物系统进行建模的基本工具。本课程重点关注海量图分析所面临的计算、算法和建模挑战。通过研究底层图结构及其特征，向学生介绍机器学习技术和数据挖掘工具，这些工具有助于揭示对各种网络的见解。

主题包括：表示学习和图神经网络；万维网算法；对知识图进行推理；影响力最大化；疾病爆发检测、社交网络分析。

课程官网：

（最新秋季课程）https://web.stanford.edu/class/cs224w/

（春季课程）https://snap.stanford.edu/class/cs224w-2023/

视频资源不开放

视频：

2023年春季斯坦福CS224W《图机器学习》 ——同济子豪兄中文精讲：

https://github.com/TommyZihao/zihao_course/tree/main/CS224W
https://space.bilibili.com/1900783/channel/collectiondetail?sid=915098
【【中英字幕】2021年春季斯坦福大学Stanford CS224W《图机器学习Machine Learning with Graphs》课程】 https://www.bilibili.com/video/BV1s54y1H76H/?share_source=copy_web&vd_source=be2dbc6e276c34a5d143c75d66d47b6d

2023年秋季斯坦福CS224W《图机器学习》课程团队：

2023年秋季斯坦福CS224W《图机器学习》课程内容大纲：

Date	Description	Optional Readings	Events	Deadlines
Tue, 9/26	1. Introduction [slides]
Thu, 9/28	2. Node embeddings [slides]	DeepWalk: Online Learning of Social Representations node2vec: Scalable Feature Learning for Networks Network Embedding as Matrix Factorization	Colab 0, Colab 1 out
Tue, 10/3	3. Graph neural networks [slides]	Geometric Deep Learning: the Erlangen Programme of ML Semi-Supervised Classification with Graph Convolutional Networks
Thu, 10/5	4. A general perspective on GNNs [slides]	Design Space of Graph Neural Networks Inductive Representation Learning on Large Graphs Graph Attention Networks	Homework 1 out LaTeX template
Tue, 10/10	5. GNN augmentation and training [slides]	Hierarchical Graph Representation Learning with Differentiable Pooling
Thu, 10/12	6. Theory of GNNs [slides]	How Powerful Are Graph Neural Networks?	Colab 2 out
Tue, 10/17	7. Heterogenous graphs [slides]	Modeling Relational Data with Graph Convolutional Networks Heterogeneous Graph Transformer		Colab 1 due
Thu, 10/19	8. Knowledge graphs [slides]	Translating Embeddings for Modeling Multi-relational Data Learning Entity and Relation Embeddings for Knowledge Graph Completion Embedding Entities and Relations for Learning and Inference in Knowledge Bases Complex Embeddings for Simple Link Prediction RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space	Homework 2 out LaTeX template	Homework 1 due
Tue, 10/24	9. Reasoning over knowledge graphs [slides]	Embedding Logical Queries on Knowledge Graphs Query2box: Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings Traversing Knowledge Graphs in Vector Space		Project Proposal due
Thu, 10/26	10. Fast neural subgraph matching [slides]	Network Motifs: Simple Building Blocks of Complex Networks Neural Subgraph Matching SPMiner: Frequent Subgraph Mining by Walking in Order Embedding Space	Colab 3 out	Colab 2 due
Tue, 10/31	11. GNNs for recommenders [slides]	Neural Graph Collaborative Filtering LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Thu, 11/2	12. Deep generative models for graphs [slides]	GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation	Homework 3 out LaTeX template	Homework 2 due
Tue, 11/7	ELECTION DAY - NO CLASS
Thu, 11/9	13. Advanced topics in GNNs [slides]	Position-aware Graph Neural Networks Identity-aware Graph Neural Networks Adversarial Attacks on Neural Networks for Graph Data	Colab 4 out	Colab 3 due Project Milestone due
Tue, 11/14	14. Graph Transformers [slides]
Thu, 11/16	15. Scaling to large graphs [slides]	Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks Simplifying Graph Convolutional Networks	Colab 5 out	Homework 3 due
Tue, 11/21	BREAK
Tue, 11/23	BREAK
Tue, 11/28	16. Geometric deep learning [slides]	SchNet: A continuous-filter convolutional neural network for modeling quantum interactions Equivariant message passing for the prediction of tensorial properties and molecular spectra Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation
Wed, 11/29	Exam			11/29 5 PM - 12/1 5 AM
Thu, 11/30	17. Link Prediction and Causality [slides]			Colab 4 due
Tue, 12/5	18. Algorithmic reasoning with GNNs [slides]	LIME: Local Interpretable Model-Agnostic Explanations A Unified Approach to Interpreting Model Predictions GNNExplainer Explainability in Graph Neural Networks: A Taxonomic Survey Trustworthy Graph Neural Networks GraphFramEx		Colab 5 due
Thu, 12/7	19. Conclusion [slides]
Thu, 12/14				Project Report due

2023年春季斯坦福CS224W《图机器学习》课程团队：

2023年春季斯坦福CS224W《图机器学习》课程内容大纲：

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课程 | 2023年秋季斯坦福CS224W《图机器学习》 | Jure Leskovec

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