COURSE DESCRIPTION
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. Topics include: representation learning and Graph Neural Networks; algorithms for the World Wide Web; reasoning over Knowledge Graphs; influence maximization; disease outbreak detection, social network analysis.
Course Features
- Lectures 47
- Quiz 0
- Duration 30 hours
- Skill level All levels
- Language English
- Students 25
- Assessments Yes
Curriculum
- 1 Section
- 47 Lessons
- 10 Weeks
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- ML with Graphs47
- 1.1Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 1.1 – Why Graphs
- 1.2Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 1.2 – Applications of Graph ML
- 1.3Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 1.3 – Choice of Graph Representation
- 1.4Stanford CS224W: ML with Graphs | 2021 | Lecture 2.1 – Traditional Feature-based Methods: Node
- 1.5Stanford CS224W: ML with Graphs | 2021 | Lecture 2.2 – Traditional Feature-based Methods: Link
- 1.6Stanford CS224W: ML with Graphs | 2021 | Lecture 2.3 – Traditional Feature-based Methods: Graph
- 1.7Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 3.1 – Node Embeddings
- 1.8Stanford CS224W: ML with Graphs | 2021 | Lecture 3.2-Random Walk Approaches for Node Embeddings
- 1.9Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 3.3 – Embedding Entire Graphs
- 1.10Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 4.1 – PageRank
- 1.11Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 4.2 – PageRank: How to Solve?
- 1.12Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 4.3 – Random Walk with Restarts
- 1.13Stanford CS224W: ML with Graphs | 2021 | Lecture 4.4 – Matrix Factorization and Node Embeddings
- 1.14Stanford CS224W: Machine Learning w/ Graphs I 2023 I Graph Neural Networks
- 1.15Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 7.1 – A general Perspective on GNNs
- 1.16Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 7.2 – A Single Layer of a GNN
- 1.17Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 7.3 – Stacking layers of a GNN
- 1.18Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 8.1 – Graph Augmentation for GNNs
- 1.19Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 8.2 – Training Graph Neural Networks
- 1.20Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 8.3 – Setting up GNN Prediction Tasks
- 1.21Stanford CS224W: ML with Graphs | 2021 | Lecture 9.1 – How Expressive are Graph Neural Networks
- 1.22Stanford CS224W: ML with Graphs | 2021 | Lecture 9.2 – Designing the Most Powerful GNNs
- 1.23Stanford CS224W: Machine Learning w/ Graphs I 2023 I Label Propagation on Graphs
- 1.24Stanford CS224W: Machine Learning w/ Graphs I 2023 I Machine Learning with Heterogeneous Graphs
- 1.25Stanford CS224W: Machine Learning w/ Graphs I 2023 I Knowledge Graph Embeddings
- 1.26Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 11.1 – Reasoning in Knowledge Graphs
- 1.27Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 11.2 – Answering Predictive Queries
- 1.28Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 11.3 – Query2box: Reasoning over KGs
- 1.29Stanford CS224W: ML with Graphs | 2021 | Lecture 12.1-Fast Neural Subgraph Matching & Counting
- 1.30Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 12.2 – Neural Subgraph Matching
- 1.31Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 12.3 – Finding Frequent Subgraphs
- 1.32Stanford CS224W: ML with Graphs | 2021 | Lecture 13.1 – Community Detection in Networks
- 1.33Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 13.2 – Network Communities
- 1.34Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 13.3 – Louvain Algorithm
- 1.35Stanford CS224W: ML with Graphs | 2021 | Lecture 13.4 – Detecting Overlapping Communities
- 1.36Stanford CS224W: Machine Learning w/ Graphs I 2023 I GNNs for Recommender Systems
- 1.37Stanford CS224W: ML with Graphs | 2021 | Lecture 15.1 – Deep Generative Models for Graphs
- 1.38Stanford CS224W: ML with Graphs | 2021 | Lecture 15.2 – Graph RNN: Generating Realistic Graphs
- 1.39Stanford CS224W: ML with Graphs | 2021 | Lecture 15.3 – Scaling Up & Evaluating Graph Gen
- 1.40Stanford CS224W: ML with Graphs | 2021 | Lecture 15.4 – Applications of Deep Graph Generation
- 1.41Stanford CS224W: Machine Learning w/ Graphs I 2023 I Advanced Topics in GNNs
- 1.42Stanford CS224W: ML with Graphs | 2021 | Lecture 17.1 – Scaling up Graph Neural Networks
- 1.43Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 17.2 – GraphSAGE Neighbor Sampling
- 1.44Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 17.3 – Cluster GCN: Scaling up GNNs
- 1.45Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 17.4 – Scaling up by Simplifying GNNs
- 1.46Stanford CS224W: Machine Learning w/ Graphs I 2023 I Geometric Graph Learning, Minkai Xu
- 1.47Stanford CS224W: Machine Learning w/ Graphs I 2023 I Trustworthy Graph AI, Rex Ying
