Framework : Tensorflow 2.4, Spektral
$ pip install requirements.txt
| Raw Graph | Raw Data |
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| DNN Model | DNN Train Graph |
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| GCN | GCN Train Graph |
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| Inception GCN Model | Inception GCN Train Graph |
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| Residual GCN Model | Residual GCN Train Graph |
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| GatedSkipConnection GCN Model | GatedSkipConnection GCN Train Graph |
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| Attention GCN Model | Attention GCN Train Graph |
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| Residual Inception GCN Model | Residual Inception GCN Train Graph |
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| Residual Attention GCN Model | Residual Attention GCN Train Graph |
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| GatedSkipConnection Inception GCN Model | GatedSkipConnection Inception GCN Train Graph |
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| GatedSkipConnection Attention GCN Model | GatedSkipConnection Attention GCN Train Graph |
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- GRAPH CONVOLUTIONAL NETWORKS
- How powerful are Graph Convolutions?
- Getting the Intuition of Graph Neural Networks
- Understanding Graph Convolutional Networks for Node Classification
- Training Graph Convolutional Networks on Node Classification Task
- Euclidean Space
- Into the Wild: Machine Learning In Non-Euclidean Spaces
- Spatial vs Spectral 1
- Beyond Graph Convolution Networks, Spatial vs Spectral 2
- Degree of the Vertex and Graph
- GCN-TF2.0
- Spatial Graph Convolutional Networks
- GNN 소개 기초부터 논문까지
- 그래프 합성곱 신경망
- Semi-Supervised Classification with Graph Convolutional Networks
- Interpretation of Symmetric Normalised Graph Adjacency Matrix?
- Precision(정밀도), Recall(재현율) and Accuracy(정확도)
- Deeply learning molecular structure-property relation- ships using attention- and gate-augmented graph con- volutional network
- GRAPH ATTENTION NETWORKS
- Spectral GCN 은… 사드세요
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