# Tobigs Graph Study

## Part 1. CS224W 

{% embed url="<http://web.stanford.edu/class/cs224w/index.html#schedule>" %}

| 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&#x20;
* Wavelets on Graphs via Spectral Graph Theory

## Part 3. Graph + Domain

* Audio
* Recommendation
* Vision
* Machine learning
* Anomaly


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