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Introduction to Graph Neural Networks. Zhiyuan LiuЧитать онлайн книгу.

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4.2 Model

4.3 Limitations

5 Graph Convolutional Networks

5.1 Spectral Methods

5.1.1 Spectral Network

5.2 Spatial Methods

5.2.1 Neural FPs

5.2.2 PATCHY-SAN

5.2.7 GraphSAGE

6 Graph Recurrent Networks

6.1 Gated Graph Neural Networks

6.4 Sentence LSTM

7 Graph Attention Networks

8 Graph Residual Networks

8.1 Highway GCN

8.2 Jump Knowledge Network

9 Variants for Different Graph Types

9.1 Directed Graphs

9.2 Heterogeneous Graphs

9.3 Graphs with Edge Information

9.4 Dynamic Graphs

9.5 Multi-Dimensional Graphs

10 Variants for Advanced Training Methods

10.2 Hierarchical Pooling

10.3 Data Augmentation

10.4 Unsupervised Training

11 General Frameworks

11.1 Message Passing Neural Networks

11.2 Non-local Neural Networks

11.3 Graph Networks

12 Applications – Structural Scenarios

12.2 Chemistry and Biology

12.2.1 Molecular Fingerprints

12.2.2 Chemical Reaction Prediction

12.2.3 Medication Recommendation

12.2.4 Protein and Molecular Interaction Prediction

12.3 Knowledge Graphs

12.3.1 Knowledge Graph Completion

12.3.2 Inductive Knowledge Graph Embedding

12.3.3 Knowledge Graph Alignment

12.4 Recommender Systems

12.4.1 Matrix Completion

12.4.2 Social Recommendation

13 Applications – Non-Structural Scenarios

13.1.1 Image Classification

13.1.2 Visual Reasoning

13.1.3 Semantic Segmentation

13.2.1 Text Classification

13.2.2 Sequence Labeling

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