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Tobigs Graph Study
  • Tobigs Graph Study
  • Chapter1. Machine Learning with Graphs
    • Python code - graph basic
  • Chapter2. Properties of Networks, Random Graph Models
    • Python code - kronecker product
  • Chapter3. Motifs and Structural Roles in Networks
    • Python code - RoIX & ESU Tree
  • Chapter4. Community Structure in Networks
  • Chapter5. Spectral Clustering
  • Chapter6. Message Passing and Node Classification
  • Chapter7. Graph Representation Learning
  • Chapter8. Graph Neural Networks
  • Chapter9. Graph Neural Networks:Hands-on Session
  • Chapter10. Deep Generative Models for Graphs
  • Chapter11. Link Analysis: PageRank
  • Chapter12. Network Effects and Cascading Behavior
  • Chapter13. Probabilistic Contagion and Models of influnce
  • Chapter14. Influence Maximization in Networks
  • Chapter15. Outbreak Detection in Networks
  • Chapter16. network evolution graph
  • Chapter17. Reasoning over Knowledge Graphs
  • Chapter18. Limitations of Graph Neural Networks
  • Chapter19. Applications of Graph Neural Networks
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  • Part 1. CS224W
  • Part 2. Paper Review
  • Part 3. Graph + Domain

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Tobigs Graph Study

NextChapter1. Machine Learning with Graphs

Last updated 4 years ago

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Part 1. CS224W

Description

WHO

Date

1.Introduction; Structure of graph

λ°°μœ λ‚˜.

4.11

2. Properties of Networks and Random Graph Models

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4.11

3. Motifs and Structural Roles in Networks

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4.11

4. Communnity Structure in Networks

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4.11

5. Spectral Clustering

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4.18

6. Message Passing and Node Classification

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4.18

7. Graph Representation Learning

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4.25

8. Graph Neural Networks

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4.25

9. Graph Neural Networks: Hands-on Session

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4.25

10. Deep Generative Models for Graphs

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5.9

Break

-

5.2

11. Link Analysis : PageRank

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5.9

12. Network Effects and Cascading Behavior

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5.9

13. Probabilistic Contagion and Models of influnce

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5.16

14. Influence Maximization in Networks

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5.16

15. Outbreak Detection in Networks

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5.16

16. Network Evolution

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5.23

17. Reasoning over Knowledge Graphs

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5.23

18. Limitations of Graph Neural Networks

μ΄μŠΉν˜„.

5.23

19. Applications of Graph Neural Networks

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5.23

Paper Review

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5.30

Paper Review

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6.6

Paper Review

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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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