CS224W
1.1.1 Why Graphs
Why Graphs
2.1.2 Applications of Graph ML
Applications of Graph ML
3.1.3 Choice of Graph Representation
Choice of Graph Representation
4.2.1 Traditional Feature-based Methods: Node
Traditional Feature-based Methods: Node
5.2.2 - Traditional Feature-based Methods: Link
Traditional Feature-based Methods: Link
6.2.3 - Traditional Feature-based Methods: Graph
Traditional Feature-based Methods: Graph
7.3.1 - Node Embeddings
Node Embeddings
8.3.2-Random Walk Approaches for Node Embeddings
Random Walk Approaches for Node Embeddings
9.3.3 - Embedding Entire Graphs
Embedding Entire Graphs
10.4.1 - Page Rank
Page Rank
11.4.2 - Page Rank : How to Solve?
Page Rank : How to Solve?
12.4.3 - Random Walk with Restarts
Random Walk with Restarts
13.4.4 - Matrix Factorization and Node Embeddings
Matrix Factorization and Node Embeddings
14.5.1 - Message passing and Node Classification
Message passing and Node Classification
15.5.2 - Relational and Iterative Classification
Relational and Iterative Classification
16.5.3 - Collective Classification
Collective Classification
17.6.1 - Introduction to Graph Neural Networks
Introduction to Graph Neural Networks
18.6.2 - Basics of Deep Learning
Basics of Deep Learning
19.6.3 - Deep Learning for Graphs
Deep Learning for Graphs
20.7.1 - A general Perspective on GNNs
A general Perspective on GNNs
21.7.2 - A Single Layer of a GNN
A Single Layer of a GNN
22.7.3 - Stacking layers of a GNN
Stacking layers of a GNN
23.8.1 Graph Augmentation for GNNs
Graph Augmentation for GNNs
24.8.2 - Training Graph Neural Networks
Training Graph Neural Networks
25. 8.3 - Setting up GNN Prediction Tasks
Setting up GNN Prediction Tasks
26.9.1 - How Expressive are Graph Neural Networks
How Expressive are Graph Neural Networks
27.9.2 - Designing the Most Powerful GNNs
Designing the Most Powerful GNNs
28.10.1-Heterogeneous & Knowledge Graph Embedding
Heterogeneous & Knowledge Graph Embedding
29.10.2 - Knowledge Graph Completion
Knowledge Graph Completion
30.10.3 - Knowledge Graph Completion Algorithms
Knowledge Graph Completion Algorithms
31.11.1 - Reasoning in Knowledge Graphs
Reasoning in Knowledge Graphs
32.11.2 - Answering Predictive Queries
Answering Predictive Queries
33.11.3 - Query2box: Reasoning over KGs
📌 Query2box: Reasoning over KGs
34.12.1 - Fast Neural Subgraph Matching & Counting
Fast Neural Subgraph Matching & Counting
35.12.2 - Neural Subgraph Matching
Neural Subgraph Matching
36.17.1 - Scaling up Graph Neural Networks
Scaling up Graph Neural Networks
37.17.2 - GraphSAGE Neighbor Sampling
GraphSAGE Neighbor Sampling