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Graph Convolutional Neural Networks

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신동 강
신동 강

Graph Convolutional Deep Learning seminar of smart bean forum at Naver D2 Startup Factory Lounge 2019-03-07. speaker : Shin-Dong Kang

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Graph Convolutional Neural Networks 강신동 smart bean forum leader (주)지능도시 CEO ceo@idosi.com 2019-03-07 Smart Bean forum seminar at Naver D2 Startup Factory Lounge 1
Smart Bean forum 2 Facebook Group http://facebook.com/groups/smartbean2 - AI, Deep Learning etc - Industrial Market Tech Biz Cooperation - Smart City - IoT - Smart Factory - Smart City
Speaker 3 강신동 (Shin-Dong Kang) - smart bean forum leader - (주)지능도시 CEO - ceo@idosi.com - www.idosi.com - Deep Learning Tech Solution Provider - Deep Learning 용역 (연구,개발)
Deep Learning of Neural Networks 4
Machine Learning, Deep Learning 접근법 5 - 기존 방식 1. 전문 지식 기반 2. 수학적 모델링 3. 입력값 입력 4. 예측 결과 출력 - 딥러닝 방식 1. 입력값과 출력값의 측정 데이터 수집 2. 수집된 출력값의 오차 줄이도록 학습 3. 모델링 자동 완성 4. 미지의 입력값 입력 5. 예측 결과 출력 Data set is the source code !
AlphaGo 6
AlphaFold 7 Google DeepMind 과학, 생명공학, 의학, 의약 산업 아미노산 1차원 연결 -> 단백질 3차원 접힘 예측 분석
Graph Information 8 emotional recognition
Convolutional Neural Networks 9
Convolutional AutoEncoder Deep Learning
Convolution Layer 11
Convolution Operation 12
Euler 13
Graph Theory History 14 - Euler - Seven Bridges of Königsberg
Abstraction using Graph 15
Vertex, Node, Edge 16
Knowlege Graph 17
Coal Power Plant Process Graph 18
subtree pattern of graph 19 height = 2
Graph Types 20
Graph Convolution Concept 21
Graph Convolutional Neural Networks (GCN) 22
Laplacian Operator 23
Laplacian 24
Image Laplacian 25
MRI Laplacian 26
27
Adjacency Matrix (인접행렬) 28
Adjacency Matrix of Weighted Graph 29
Directed Graph Adjacency Matrix (out-direction) 30
31 out-direction
32 추천 시스템에서 나타나는 graph 입니다. 침대구매 --(0.6)-> 이불구매 --(0.8)-> 베개구매 자연현상이나 물리 화학적 공정을 표현할 때도 나타나는 graph 입니다. sun --(0.3)-> cloud --(0.1)-> rain
33
34 Gaussian distribution
Gradient of Scalar 35
Divergence of Vector 36
37
Convolution Operation 38
Laplacian 39
Graph Laplacian ( L=D-W ) 40 (in-weight)
Graph Convolution Layer 41
Graph Convolution Propagation 42Cij : normalized constant
Graph Convolution Layer 43
44
Convolution Kernel Size 45
Graph Convolution Kernel 46
Graphlet (index 0~72) 47 G pattern orbit index
Word Embedding 48
49
Graphlet Degree Vector (GDV) 50
51
Graph Downsampling & Graph Pooling 52
Heavy-edge Matching for Graph Coarsening 53 HEM
Sensor Noise Filtering with Graph 54
WL subtree kernel computing (1/5) 55 Weisfeiler-Lehman subtree method (와이스페일러-리만)
WL Multiset-labeling (2/5) 56
WL Label compression (3/5) 57
WL Relabeling (4/5) 58
WL Feature Vector (5/5) 59
Deep Graph Convolution Networks 60 WL method SortPooling Link WL convolution and Neural Networks for backpropagation
1D Convolution after SortPooling layer 61
Graph Convolutional Neural Networks (GCN) 62
63 Graph with Attention
New Protein Medicine 64
Molecular Graph 65
Molecular Fingerprint Representation 66
Protein Shape Element 67 alpha helix beta sheet
68
Protein Folding 69
Protein Folding & Energy Level 70
Protein Binding Complex 71
Protein Folding Figurative 72
Graph Representation on Protein Structure 73
AlphaFold to predict protein foldings 74
Protein Medicine Search by GCN 75
DNA Sequence Convolutional NN 76
Gene Information of DNA 77
DNA Processing with Deep Learning 78
Predicting Protein-Protein interactions 79
80
Multiple Sequence Alignment (MSA) 81
Sequence Conservation to Protein Fold Hot Spot 82
Drug Repurposing using Natural Language Text 83
Disease gene prioritization with GCN 84
Proteomic and cell biological dissection of mitochondrial networks 85
Contact 강신동 (주)지능도시 CEO ceo@idosi.com Deep Learning 관련 용역 (연구,개발) 86

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Graph Convolutional Neural Networks