# Tobigs Graph Study

## Part 1. CS224W

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

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