The document presents an overview of Graph Convolutional Neural Networks, focusing on their applications in deep learning, especially in the industrial and medical fields. It discusses various methodologies, including the process of data collection, prediction, and modeling within deep learning, as well as graph theory concepts relevant to neural networks. Notably, the document highlights the potential of GCN in predicting protein structures and interactions, linking advancements in AI with biotechnology.

1. 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

2. 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

3. 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)

4. Deep Learning of Neural Networks
4

5. 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 !

6. AlphaGo
6

7. AlphaFold
7
Google DeepMind
과학, 생명공학, 의학, 의약 산업
아미노산 1차원 연결
-> 단백질 3차원 접힘 예측 분석

8. Graph Information
8
emotional
recognition

9. Convolutional Neural Networks
9

10. Convolutional AutoEncoder Deep Learning

11. Convolution Layer
11

12. Convolution Operation
12

13. Euler
13

14. Graph Theory History
14
- Euler
- Seven Bridges of Königsberg

15. Abstraction using Graph
15

16. Vertex, Node, Edge
16

17. Knowlege Graph
17

18. Coal Power Plant Process Graph
18

19. subtree pattern of graph
19
height = 2

20. Graph Types
20

21. Graph Convolution Concept
21

22. Graph Convolutional Neural Networks (GCN)
22

23. Laplacian Operator
23

24. Laplacian
24

25. Image Laplacian
25

26. MRI Laplacian
26

27. 27

28. Adjacency Matrix (인접행렬)
28

29. Adjacency Matrix of Weighted Graph
29

30. Directed Graph Adjacency Matrix
(out-direction)
30

31. 31
out-direction

32. 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. 33

34. 34
Gaussian distribution

35. Gradient of Scalar
35

36. Divergence of Vector
36

37. 37

38. Convolution Operation
38

39. Laplacian
39

40. Graph Laplacian ( L=D-W )
40
(in-weight)

41. Graph Convolution Layer
41

42. Graph Convolution Propagation
42Cij : normalized constant

43. Graph Convolution Layer
43

44. 44

45. Convolution Kernel Size
45

46. Graph Convolution Kernel
46

47. Graphlet (index 0~72)
47
G pattern
orbit index

48. Word Embedding
48

49. 49

50. Graphlet Degree Vector (GDV)
50

51. 51

52. Graph Downsampling & Graph Pooling
52

53. Heavy-edge Matching for Graph Coarsening
53
HEM

54. Sensor Noise Filtering with Graph
54

55. WL subtree kernel computing (1/5)
55
Weisfeiler-Lehman subtree method
(와이스페일러-리만)

56. WL Multiset-labeling (2/5)
56

57. WL Label compression (3/5)
57

58. WL Relabeling (4/5)
58

59. WL Feature Vector (5/5)
59

60. Deep Graph Convolution Networks
60
WL method
SortPooling
Link WL convolution and Neural Networks
for backpropagation

61. 1D Convolution after SortPooling layer
61

62. Graph Convolutional Neural Networks (GCN)
62

63. 63
Graph with Attention

64. New Protein Medicine
64

65. Molecular Graph
65

66. Molecular Fingerprint Representation
66

67. Protein Shape Element
67
alpha helix
beta sheet

68. 68

69. Protein Folding
69

70. Protein Folding & Energy Level
70

71. Protein Binding Complex
71

72. Protein Folding Figurative
72

73. Graph Representation on Protein Structure
73

74. AlphaFold to predict protein foldings
74

75. Protein Medicine Search by GCN
75

76. DNA Sequence Convolutional NN
76

77. Gene Information of DNA
77

78. DNA Processing with Deep Learning
78

79. Predicting Protein-Protein interactions
79

80. 80

81. Multiple Sequence Alignment (MSA)
81

82. Sequence Conservation to Protein Fold Hot Spot
82

83. Drug Repurposing using Natural Language Text
83

84. Disease gene prioritization with GCN
84

85. Proteomic and cell biological dissection
of mitochondrial networks
85

86. Contact
강신동
(주)지능도시 CEO
ceo@idosi.com
Deep Learning 관련 용역 (연구,개발)
86