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[GNN] 2. GNN 이전의 Machine Learning을 활용한 Graph 학습AI/GNN 2022. 2. 13. 00:09728x90
[GNN] 2. GNN 이전의 Machine Learning을 활용한 Graph 학습
1. Graph란?
- 왜 Graph?
- Graph의 종류
- Graph의 표현
- Graph Tasks
- Graph의 Motif
2. GNN 이전의 Machine Learning을 활용한 Graph 학습
- Node Feature
- Eigenvector centrality
- Betweenness centrality
- Closeness centrality
- .....
- Link Feature
- Distance-based feature
- Local neighborhood overlap
- Global neighborhood overlap
- Graph Feature
- Graphlet kernel
- Weisfeiler-Lehman Kernel
3. Graph Representation Learning
- Node Embedding
- Graph Embedding
4. GNN
- Recurrent Graph Neural Networks (RecGNNs)
- Convolutional Graph Neural Networks (ConvGNNs)
- Spectral Model
- Spatial Model
- Graph AutoEncoders (GAEs)
- Network Embedding
- Graph Generation
- Spatial-Temporal Graph Neural Networks (STGNNs)
- CNN Based
- RNN Based
- CNN & RNN based Model
- Attention based Model
5. GNN의 Benchmark Datasets
- Citation Networks
- Biochemical Graphs
- Social Networks
- Others
6. GNN Library
- Pytorch Geometric
- DGL
Node-level Feature
Link-level Feature
Graph-level Feature
Reference
https://velog.io/@tobigsgnn1415/Traditional-Methods-for-Machine-Learning-in-Graphs
2. Traditional Methods for Machine Learning in Graphs
Traditional Methods for Machine Learning in Graphs [작성자: 이성범]
velog.io
https://harryjo97.github.io/theory/Weisfeiler-Lehman-Algorithm/
Weisfeiler-Lehman Algorithm
Weisfeiler-Lehman Algorithm
harryjo97.github.io
https://davidbieber.com/post/2019-05-10-weisfeiler-lehman-isomorphism-test/
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