Introduction to Graph Neural Networks. Zhiyuan Liu

4.2 Model

       4.3 Limitations

       5 Graph Convolutional Networks

       5.1 Spectral Methods

       5.1.1 Spectral Network

       5.1.2 ChebNet

       5.1.3 GCN

       5.1.4 AGCN

       5.2 Spatial Methods

       5.2.1 Neural FPs

       5.2.2 PATCHY-SAN

       5.2.3 DCNN

       5.2.4 DGCN

       5.2.5 LGCN

       5.2.6 MoNet

       5.2.7 GraphSAGE

       6 Graph Recurrent Networks

       6.1 Gated Graph Neural Networks

       6.2 Tree LSTM

       6.3 Graph LSTM

       6.4 Sentence LSTM

       7 Graph Attention Networks

       7.1 GAT

       7.2 GAAN

       8 Graph Residual Networks

       8.1 Highway GCN

       8.2 Jump Knowledge Network

       8.3 DeepGCNs

       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

       10.1 Sampling

       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

       12.1 Physics

       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 Image

       13.1.1 Image Classification

       13.1.2 Visual Reasoning

       13.1.3 Semantic Segmentation

       13.2 Text

       13.2.1 Text Classification

       13.2.2 Sequence Labeling

       13.2.3