# Tobigs Graph Study ## Part 1. CS224W {% embed url="" %} | 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