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

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：

DateDescriptionOptional ReadingsEventsDeadlinesTue, 9/261. Introduction
[slides]

Thu, 9/282. 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, 10/33. Graph neural networks
[slides]

• Geometric Deep Learning: the Erlangen Programme of ML

• Semi-Supervised Classification with Graph Convolutional Networks

Thu, 10/54. 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, 10/105. GNN augmentation and training
[slides]

• Hierarchical Graph Representation Learning with Differentiable Pooling

Thu, 10/126. Theory of GNNs
[slides]

• How Powerful Are Graph Neural Networks?

Colab 2 out
Tue, 10/177. Heterogenous graphs
[slides]

• Modeling Relational Data with Graph Convolutional Networks

• Heterogeneous Graph Transformer

Colab 1 dueThu, 10/198. 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 templateHomework 1 dueTue, 10/249. 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 dueThu, 10/2610. 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 outColab 2 dueTue, 10/3111. 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, 11/212. 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 templateHomework 2 dueTue, 11/7ELECTION DAY – NO CLASS

Thu, 11/913. 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 outColab 3 due
Project Milestone dueTue, 11/1414. Graph Transformers
[slides]

Thu, 11/1615. Scaling to large graphs
[slides]

• Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

• Simplifying Graph Convolutional Networks

Colab 5 outHomework 3 dueTue, 11/21BREAK

Tue, 11/23BREAK

Tue, 11/2816. 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, 11/29Exam

11/29 5 PM – 12/1 5 AMThu, 11/3017. Link Prediction and Causality
[slides]

Colab 4 dueTue, 12/518. 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 dueThu, 12/719. 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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