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Graph Neural Network (한국어)

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Jungwon Kim
Jungwon Kim

Graph Neural Network의 기본적인 개념과 소개에 대한 슬라이드입니다.

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Graph Neural Network 2019.03.24 Jungwon Kim
시작하기 전... ● GCN(Graph Convolutional Network)에 대한 이야기가 아닙니다 ○ 추후에 볼 예정… ● GNN의 기본 컨셉에 대해서만 다룹니다
Graph Neural Network란? ● Graph 데이터 구조를 위한 Neural Network ○ 입력: Graph ○ 출력: Label ■ 숫자(0.95, 0.81, 0.4, …) ■ 라벨링 (Protein, Carbon-dioxide, etc.) ■ 분류 (Drug/Not Drug, etc.)
왜 Graph Neural Network? ● 다양한 관계에 대한 이해를 위한 구조 ○ RNN의 경우 Cell간에 Recursive한 관계를 가짐 ● 네트워크를 표현하기 위함 ○ 자기 자신에 대한 Connection ○ 어떤것들은 연결이 아예 존재하지 않는 경우도 있음
Euclidean Space (유클리드 공간) 원점에서 좌표 (x, y, z)까지의 거리 = 원점과 좌표 사이의 가장 짧은 직선의 길이
Non-Euclidean Space (비 유클리드 공간) 파랑부분의 처음 지점 ~ 붉은부분의 끝지점 ● Euclidean 공간에서는 거리가 계속 변함 ○ 거리가 계속 좁혀지는 형태 ● Non-Euclidean 공간에서는 거리가 변하지 않음 https://deepmind.com/blog/alphafold/
Euclidean Space vs Non-Euclidean Space
When to use GNN? ● 화학 ○ 마약인가/아닌가? (확률을 통해 산출) ● Program 이해 ○ Expression Statement ○ 프로그래밍을 통한 해결 ○ RNN 모델보다 성능이 더 좋음 ● Etc.
For example...
For example...
RNN (Recurrent Neural Network) ● RNN에서는 각 Cell이 이전 Cell에서부터 전달되는 형식으로 정보가 누적된다 ○ 이러한 연속된 형태의 예시로는... ■ Time Series ■ 단어 순서 ■ 사건의 시퀀스 ■ Etc.
RNN (Recurrent Neural Network)
GNN (Graph Neural Network)
GNN (Graph Neural Network)
GNN (Graph Neural Network) Adjacency = 1
GNN (Graph Neural Network)
Adjacency Matrix ● 연결 강도로 시각화
Adjacency Matrix DeepMind: AlphaFold (https://deepmind.com/blog/alphafold/#image-33420)
GCN (Graph Convolutional Network) ● (l): Layer ● H: Input from Layer (l) ● W: Weights (Trainable) ● A: Adjacency Matrix ● D: Degree Matrix ○ Number of connections attached to each node ● Hat notations ○ Normalized with standard Identity matrix ● Activation Function: Sigmoid
GCN (Graph Convolutional Network)
GNN (Graph Neural Network) ● RNN ○ 현재 Cell에 이전 Cell의 정보를 전달하는 방식으로 propagate ● GNN ○ ‘Adjacency’에 대해 고려 ■ Adjacency = 1에 대한 정보 ■ Adjacency = 2에 대한 정보 ■ Adjacency = 3에 대한 정보 ■ … ■ Adjacency = n(최대 갯수)에 대한 정보 까지
GNN Variations ● Graph Neural Network (2005) ● Spectral Networks (2014) ● Neural Message Passing (2017) ○ Gated Graph Neural Network (2016) ○ ChebyNets (2016) ○ Graph Convolution Network (2017) ● Async. Neural Message Passing (2018) ● AMPNet (2018) ● Programs As Graphs (2018)
GNN Variations ● Graph AutoEncoder (2016) ● GraphGAN (2017) ● GraphSAGE (2017) ● Graph Attention Networks (ICRL 2018) ● GraphRNN (2018) ● Splitter (2019)
Types of GNN
Datasets for GNN https://github.com/shiruipan/graph_datasets ** Not listed in link, but ‘Zachary’s karate club’ is a commonly used social network. ** (https://towardsdatascience.com/how-to-do- deep-learning-on-graphs-with-graph- convolutional-networks-7d2250723780)
Graph Nets Library (DeepMind) https://github.com/deepmind/graph_nets ● DeepMind에서 제작한 프레임워크 ● Doc이 상세하게 기술되어 있음 ● Tensorflow-based
DGL (Deep Graph Library) https://www.dgl.ai/ ● 최근 개발됨 (2018) ● NYU, NYU-상하이, Amazon 에서 개발
PyTorch Geometric https://github.com/rusty1s/pytorch_geometric ● PyTorch Extension ○ 기본 설치 X ● DGL보다 최대 15배 가량 빠 르다는 정보가 있음
Geometric Deep Learning http://geometricdeeplearning.com/ ● Not a Framework ● Many references for GNN (Not a lot of descriptions)
References Microsoft - Graph Neural Networks: Variations and Applications (https://www.youtube.com/watch?v=cWIeTMklzNg) Graph Theory - Adjacency Matrices (https://www.ebi.ac.uk/training/online/course/network-analysis-protein-interaction-data- introduction/introduction-graph-theory/graph-0) DeepMind - AlphaFold (https://deepmind.com/blog/alphafold/)

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Graph Neural Network (한국어)

  • 2. 시작하기 전... ● GCN(Graph Convolutional Network)에 대한 이야기가 아닙니다 ○ 추후에 볼 예정… ● GNN의 기본 컨셉에 대해서만 다룹니다
  • 3. Graph Neural Network란? ● Graph 데이터 구조를 위한 Neural Network ○ 입력: Graph ○ 출력: Label ■ 숫자(0.95, 0.81, 0.4, …) ■ 라벨링 (Protein, Carbon-dioxide, etc.) ■ 분류 (Drug/Not Drug, etc.)
  • 4. 왜 Graph Neural Network? ● 다양한 관계에 대한 이해를 위한 구조 ○ RNN의 경우 Cell간에 Recursive한 관계를 가짐 ● 네트워크를 표현하기 위함 ○ 자기 자신에 대한 Connection ○ 어떤것들은 연결이 아예 존재하지 않는 경우도 있음
  • 5. Euclidean Space (유클리드 공간) 원점에서 좌표 (x, y, z)까지의 거리 = 원점과 좌표 사이의 가장 짧은 직선의 길이
  • 6. Non-Euclidean Space (비 유클리드 공간) 파랑부분의 처음 지점 ~ 붉은부분의 끝지점 ● Euclidean 공간에서는 거리가 계속 변함 ○ 거리가 계속 좁혀지는 형태 ● Non-Euclidean 공간에서는 거리가 변하지 않음 https://deepmind.com/blog/alphafold/
  • 7. Euclidean Space vs Non-Euclidean Space
  • 8. When to use GNN? ● 화학 ○ 마약인가/아닌가? (확률을 통해 산출) ● Program 이해 ○ Expression Statement ○ 프로그래밍을 통한 해결 ○ RNN 모델보다 성능이 더 좋음 ● Etc.
  • 11. RNN (Recurrent Neural Network) ● RNN에서는 각 Cell이 이전 Cell에서부터 전달되는 형식으로 정보가 누적된다 ○ 이러한 연속된 형태의 예시로는... ■ Time Series ■ 단어 순서 ■ 사건의 시퀀스 ■ Etc.
  • 13. GNN (Graph Neural Network)
  • 14. GNN (Graph Neural Network)
  • 15. GNN (Graph Neural Network) Adjacency = 1
  • 16. GNN (Graph Neural Network)
  • 17. Adjacency Matrix ● 연결 강도로 시각화
  • 18. Adjacency Matrix DeepMind: AlphaFold (https://deepmind.com/blog/alphafold/#image-33420)
  • 19. GCN (Graph Convolutional Network) ● (l): Layer ● H: Input from Layer (l) ● W: Weights (Trainable) ● A: Adjacency Matrix ● D: Degree Matrix ○ Number of connections attached to each node ● Hat notations ○ Normalized with standard Identity matrix ● Activation Function: Sigmoid
  • 21. GNN (Graph Neural Network) ● RNN ○ 현재 Cell에 이전 Cell의 정보를 전달하는 방식으로 propagate ● GNN ○ ‘Adjacency’에 대해 고려 ■ Adjacency = 1에 대한 정보 ■ Adjacency = 2에 대한 정보 ■ Adjacency = 3에 대한 정보 ■ … ■ Adjacency = n(최대 갯수)에 대한 정보 까지
  • 22. GNN Variations ● Graph Neural Network (2005) ● Spectral Networks (2014) ● Neural Message Passing (2017) ○ Gated Graph Neural Network (2016) ○ ChebyNets (2016) ○ Graph Convolution Network (2017) ● Async. Neural Message Passing (2018) ● AMPNet (2018) ● Programs As Graphs (2018)
  • 23. GNN Variations ● Graph AutoEncoder (2016) ● GraphGAN (2017) ● GraphSAGE (2017) ● Graph Attention Networks (ICRL 2018) ● GraphRNN (2018) ● Splitter (2019)
  • 25. Datasets for GNN https://github.com/shiruipan/graph_datasets ** Not listed in link, but ‘Zachary’s karate club’ is a commonly used social network. ** (https://towardsdatascience.com/how-to-do- deep-learning-on-graphs-with-graph- convolutional-networks-7d2250723780)
  • 26. Graph Nets Library (DeepMind) https://github.com/deepmind/graph_nets ● DeepMind에서 제작한 프레임워크 ● Doc이 상세하게 기술되어 있음 ● Tensorflow-based
  • 27. DGL (Deep Graph Library) https://www.dgl.ai/ ● 최근 개발됨 (2018) ● NYU, NYU-상하이, Amazon 에서 개발
  • 28. PyTorch Geometric https://github.com/rusty1s/pytorch_geometric ● PyTorch Extension ○ 기본 설치 X ● DGL보다 최대 15배 가량 빠 르다는 정보가 있음
  • 29. Geometric Deep Learning http://geometricdeeplearning.com/ ● Not a Framework ● Many references for GNN (Not a lot of descriptions)
  • 30. References Microsoft - Graph Neural Networks: Variations and Applications (https://www.youtube.com/watch?v=cWIeTMklzNg) Graph Theory - Adjacency Matrices (https://www.ebi.ac.uk/training/online/course/network-analysis-protein-interaction-data- introduction/introduction-graph-theory/graph-0) DeepMind - AlphaFold (https://deepmind.com/blog/alphafold/)