Machine learning with graph
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This document provides an overview of machine learning with graphs. It discusses graph neural networks and deep learning in graphs. It covers representing graphs using adjacency matrices and lists. It also discusses node and graph level features, as well as node embeddings using random walks. Finally, it summarizes several graph neural network models like GCN and GraphSAGE and their applications to citation networks, social networks, and knowledge graphs.
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Machine learning with graph
- 1. Machine Learning with Graph Graph Neural Networks (GNN) Ding Li 2021.10
- 2. 2 CS224W: Machine Learning with Graphs Prof. Jure Leskovec @ Stanford
- 3. 3 Deep Learning in Graph
- 4. 4 Graph Components and Types
- 6. 6 Representing Graph 1. Adjacency Matrix 2. Edge List 3. Adjacency List
- 8. 8
- 9. 9 Node Features: Clustering Coefficient & Graphlets Clustering Coefficient
- 11. 11 Graph-Level Features: Kernel Methods
- 13. 13 Weisfeiler-Lehman Kernel (Color Refinement, computationally efficient)
- 14. 14 Node Embeddings: Encoder and Decoder
- 15. 15 Random Walk for Node Embeddings
- 16. 16 Random Walk Optimization & Stochastic Gradient Descent Stochastic Gradient Descent Perozzi 2014
- 17. 17 Node2vec: Biased Walks Grover 2016 Breadth First Search Depth First Search
- 18. 18 Embedding Entire Graphs Approach 3: Anonymous Walk Embeddings
- 19. 19 Anonymous Walk Embeddings Ivanov 2018
- 21. 21 PageRank: Power Iteration Method
- 23. 23
- 24. 24 Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec Qiu 2018
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- 29. 29 A: adjacency matrix Model can be of arbitrary depth
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- 32. 32 Graphic Convolutional Networks (GCN) Kipf 2017
- 33. 33 Kipf 2017 Adjacency matrix Degree matrix Input-to-hidden weight matrix with H feature hidden-to-output weight matrix with F channels Citation networks: Citeseer, Cora and Pubmed Features: bag-of-words feature vectors Label: each document has a label, total 20 labels NELL: a dataset extracted from a knowledge graph Separated relation nodes were assigned for each entity (e1,r,e2) →(e1, r1), (e2,r2) Features: bag-of-words feature vectors Label: node label Propagation of feature information from neighboring nodes in every layer improves classification performance in comparison to previous methods, where only label information is aggregated. Results for all other baseline methods are taken from the Planetoid paper (Yang et al., 2016). Planetoid* denotes the best model for the respective dataset out of the variants presented in their paper.
- 34. 34 Hamilton 2018 Citation data: predicting paper subject categories (6) on a large citation dataset Features: i node degrees; ii word embedding on abstract Reddit data: predicting which community different Reddit posts belong to Posts were connected if the same user comments on both Features: i embedding of title; ii embedding on comments; iii post’s score; iv number of comments Protein-protein interactions: predicting the protein roles (121) in terms of their cellular functions Features: i positional gene sets; ii motif gene sets; iii immunological signatures
- 35. 35 Ying 2018 I (2 billion pins) ↔ C (1 billion board) Method Hit-rate Visual embeddings (4,096 dimensions, from CNN) 17% Annotation embeddings (256 dim, title & description -> Word2Vec) 14% Combined embeddings (2-layer MLP on visual and annotation embeddings) 27% Pixie (random-walk-based, closeness only from graph structure) - PinSage (graph convolution with visual and annotation features) 67% Hit-rate: probability that positive samples were ranked among the top 500 among the 5M negative samples Importance pooling: based upon random walk similarity to choose positive sampling, leading to a 46% performance gain in offline evaluation metrics. Curriculum training: the algorithm is fed harder-and-harder examples (from PageRank score) during training, resulting in a 12% performance gain. Pinterest A/B tests show 30% to 100% improvements in user engagement across various settings after deploying PinSage
- 36. 36 Velickovic 2018 K multi-head attention Concatenation Averaging (final layer) GitHub
- 37. 37 For the inductive task, two unseen test graphs were tested.
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- 41. 41 Ying 2018
- 42. 42 DeepSNAP provides core modules for this pipeline GraphGym further implements the full pipeline to facilitate GNN design
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- 48. 48
- 49. 49
- 50. 50 Xu 2019
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- 52. 52 Block Diagonal Matrices Basis Learning
- 53. 53
- 54. 54
- 56. 56
- 57. 57 Lin 2015
- 58. 58 Lin 2015
- 59. 59 Lin 2015
- 60. 60 Lin 2015
- 62. 62 Lin 2015 KG Embeddings in Practice
- 63. 63 Path Queries
- 64. 64
- 65. 65 Ren 2020
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- 70. 70 z-score: classify subgraph “significance” • Negative: under-representation • Positive: over-representation
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- 74. 74
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- 77. 77 Yang 2013
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- 82. 82 Watts 1998
- 83. 83 EPINIONS: who-trusts-whom social network Leskovec 2010
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- 86. 86 You 2018
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- 89. 89 You 2018
- 90. 90 You 2018
- 91. 91 You 2019
- 92. 92 You 2021
- 93. 93
- 94. 94 Wu 2019 The accuracy of Simple Graph Convolution (SGC) is similar as GCN’s
- 95. 95 Zitnik 2018
- 96. 96 Alsentzer 2020 Li 2021
- 97. 97 Gysi 2021
- 98. 98 CS224W Colab • Colab 0: Networkx, PyTorch Geometric, & GCN • Colab 1: Node Embeddings • Colab 2: GCN Implementation • Colab 3: GraphSAGE Implementation