文章分类 -  Graph

摘要:1. Reachability Matrix 1.2 Powers of tadjacency matrix Powers of the graph adjacency matrix: Computing $#\text{walks} between two nodes $A_{uv} = 1$ i 阅读全文
posted @ 2023-02-22 15:08 veager 阅读(40) 评论(0) 推荐(0)
摘要:Network Science: Measures and metrics 0 Denotation \(\mathcal{U} = \mathcal{V} = \{ v_1, v_2, \cdots, v_N \}\), node set \(\mathcal{E} = \{e_{uv} | u, 阅读全文
posted @ 2023-01-29 22:22 veager 阅读(90) 评论(0) 推荐(0)
摘要:Implement Graph Neural Networks Model 1 Graph Convolutional Networks (GCN) 1.1 Formulation The propagation function $f$: $$ \mathbf{H}^{(l+1)} = f \le 阅读全文
posted @ 2022-07-25 20:49 veager 阅读(70) 评论(0) 推荐(0)
摘要:Stanford CS224W : Machine Learning with Graphs, Fall 2021: 9 Theory of Graph Neural Networks site 1. Setting-up GNN Prediction Tasks 1.1 GNN Training 阅读全文
posted @ 2022-07-17 19:41 veager 阅读(492) 评论(0) 推荐(0)
摘要:Stanford CS224W : Machine Learning with Graphs, Fall 2021: 8 Applications of Graph Neural Networks site 1. GNN Augmentation and Training 2. Stacking G 阅读全文
posted @ 2022-07-17 19:39 veager 阅读(102) 评论(0) 推荐(0)
摘要:Stanford CS224W : Machine Learning with Graphs, Fall 2021: 7 Graph Neural Networks 2: Design Space site 1. A General Perspective on GNNs 1.1 A General 阅读全文
posted @ 2022-07-14 17:32 veager 阅读(120) 评论(0) 推荐(0)
摘要:Stanford CS224W : Machine Learning with Graphs, Fall 2021: 6 Graph Neural Networks site 1. Overview 1.1 Node Embeddings Limitations of shallow embeddi 阅读全文
posted @ 2022-07-13 20:53 veager 阅读(73) 评论(0) 推荐(0)
摘要:Stanford CS224W : Machine Learning with Graphs, Fall 2021: 5 Message Passing and Node Classification site 1. Overview 2. Leverage Node Correlations in 阅读全文
posted @ 2022-07-13 17:39 veager 阅读(27) 评论(0) 推荐(0)
摘要:Stanford CS224W : Machine Learning with Graphs, Fall 2021: 4 Graph as Matrix: PageRank, Random Walks and Embeddings site 1. Overview Treating a graph 阅读全文
posted @ 2022-07-13 16:09 veager 阅读(100) 评论(0) 推荐(0)
摘要:Machine Learning with Graphs : 3 Node Embeddings 1. Overview Graph Representation Learning alleviates the need to do feature engineering every single 阅读全文
posted @ 2022-07-10 19:35 veager 阅读(167) 评论(0) 推荐(0)
摘要:PyTorch Geometric (PyG) 1. 安装 安装:site import os import torch print(torch.__version__) os.environ['TORCH'] = torch.__version__ ! pip install -q torch-s 阅读全文
posted @ 2022-07-09 10:50 veager 阅读(572) 评论(0) 推荐(0)
摘要:Stanford CS224W : Machine Learning with Graphs, Fall 2021: 2 Traditional Methods for ML on Graphs site 1. Introduction Traditional ML pipeline uses ha 阅读全文
posted @ 2022-07-08 20:20 veager 阅读(249) 评论(0) 推荐(0)
摘要:Stanford CS224W : Machine Learning with Graphs, Fall 2021: 1 Introduction; Machine Learning for Graphs site 1. Classic Graph ML tasks Node classificat 阅读全文
posted @ 2022-07-08 20:05 veager 阅读(78) 评论(0) 推荐(0)
摘要:Minimum Spanning Tree Only used for undirected graph 1. Kruskal's Algorithm 1.1 Description 1.2 Procedure Suppose: \(G\) : Graph \(|N|\) : Node set of 阅读全文
posted @ 2022-03-30 09:03 veager 阅读(84) 评论(0) 推荐(0)
摘要:Shortest Path Algorithm 1. Dijkstra's algorithm 1.1 Description Application scope It is available for both directed and non-directed networks with non 阅读全文
posted @ 2022-03-30 09:01 veager 阅读(56) 评论(0) 推荐(0)
摘要:Graph Model Github : site 1. Definition Nodes: $N$ Arcs (or branches, or edges) $A$ Directed arcs (or oriented arcs) Undirected arcs (or unoriented ar 阅读全文
posted @ 2022-03-30 08:52 veager 阅读(296) 评论(0) 推荐(0)