Some GNNs are implemented using PyG for node classification tasks, including: GCN, GraphSAGE, SGC, GAT, R-GCN and HAN (Heterogeneous Graph Attention Network), which will continue to be updated in the future.

Some GNNs are implemented using PyG for link prediction tasks, including: GCN, GraphSAGE, GAT, Node2Vec、RGCN、HGT and HAN, which will continue to be updated in the future.

PyG (a geometric extension library for PyTorch) implementation of several Graph Neural Networks (GNNs): GCN, GAT, GraphSAGE, etc.

使用Pytorch Geometric（PyG）实现了Cora、Citeseer、Pubmed数据集上的GraphSAGE模型（full-batch）

This column has sorted out "Graph neural network code Practice", which contains related code implementation of different graph neural networks (PyG and self-implementation), combining theory with practice, such as GCN, GAT, GraphSAGE and other classic graph networks, each code instance is attached with complete code.

Graph neural network/Graph Embedding experimental code. Refer to the implementation from DGL/PyG examples, including gnn: gcn, graphSAGE, gat, ge: node2vec.

需要的包已经在requirements.txt中，注意先安装torch，再根据torch安装PyG，PyG官网可以根据torch版本配置安装指令

需要的包已经在requirements.txt中，注意先安装torch，再根据torch安装PyG，PyG官网可以根据torch版本配置安装指令

Compares GraphSAGE and GAT in PyTorch (PyG) to suggest top-K friends on a social graph. Includes data prep & edge splits, mini-batch training with negative sampling, evaluation (ROC-AUC, AP, Hits@K), inference for candidate ranking, and reproducible notebooks/scripts for experimentation and tuning.