Tobigs Graph Study

Part 1. CS224W

CS224W | Home

Description	WHO	Date
1.Introduction; Structure of graph	배유나.	4.11
2. Properties of Networks and Random Graph Models	박진혁.	4.11
3. Motifs and Structural Roles in Networks	이승현.	4.11
4. Communnity Structure in Networks	이예지.	4.11
5. Spectral Clustering	박진혁..	4.18
6. Message Passing and Node Classification	배유나.	4.18
7. Graph Representation Learning	신윤종..	4.25
8. Graph Neural Networks	이승현.	4.25
9. Graph Neural Networks: Hands-on Session	이예지.	4.25
10. Deep Generative Models for Graphs	신윤종.	5.9
Break	-	5.2
11. Link Analysis : PageRank	배유나.	5.9
12. Network Effects and Cascading Behavior	박진혁.	5.9
13. Probabilistic Contagion and Models of influnce	이승현.	5.16
14. Influence Maximization in Networks	이예지.	5.16
15. Outbreak Detection in Networks	신윤종.	5.16
16. Network Evolution	배유나.	5.23
17. Reasoning over Knowledge Graphs	박진혁.	5.23
18. Limitations of Graph Neural Networks	이승현.	5.23
19. Applications of Graph Neural Networks	이예지.	5.23
Paper Review	신윤종.	5.30
Paper Review	배유나.	6.6
Paper Review	박진혁.	6.13

Part 2. Paper Review

• Semi-Supervised Classification with Graph Convolutional Networks

• Inductive Representation Learning on Large Graphs

• Graph Attention Networks

• Learning with Local and Global Consistency

• How powerful are graph neural networks?

• Simplifying Graph Convolutional Networks

• Graph wavelet neural network

• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

• Wavelets on Graphs via Spectral Graph Theory

Part 3. Graph + Domain

• Audio

• Recommendation

• Vision

• Machine learning

• Anomaly