PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.

A PyTorch implementation of GraphSAGE. This package contains a PyTorch implementation of GraphSAGE.

Representation learning on large graphs using stochastic graph convolutions.

A PyTorch implementation of of E-GraphSAGE.

The pytorch implementation of E-GraphSAGE and E-ResGAT, two solutions for intrusion detection.

An PyTorch implementation of graph neural networks (GCN, GraphSAGE and GAT) that can be simply imported and used.

The code for GCN, GAT and Graphsage based on pytorch.

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

PyTorch implementation of GraphSAGE.

这是GraphSAGE模型在Cora、Citeseer、Pubmed数据集上的复现代码。语言：PyTorch

Graph neural networks tutorial in pytorch (GCN, GAT, Node2vec, GraphSAge, ClusterGCN, ...)

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

pytorch implementation of graph models, including gcn, gat, graphsage etc.

A Network Intrusion Detection System (NIDS) based on a Graph Neural Network (GNN), using E-GraphSAGE, implemented with PyTorch and PyTorch Geometric.

A simple notebook utilizing PyTorch-Geometric for node classification on Zachary's Karate Club dataset using GCN, GraphSAGE, and GAT based graph models.

GraphSAGE implemented by pytorhc.

The transductive version of graphsage-pytorch from https://github.com/twjiang/graphSAGE-pytorch

Graphsage model on cora dataset (pytorch+jupyter) 🐓

GraphSAGE of Pytorch version.

使用pytorch运行GNN代码，包括GCN、GraphSAGE、GAT等。

Graph Neural Networks (GNNs) for Node Classification using Graph Attention Networks (GAT) and GraphSAGE, implemented in PyTorch and TensorFlow

一些图嵌入和图神经网络(GNN)模型 | 包括DeepWalk，Node2Vec，GCN，GraphSAGE，GAT... | 纯Pytorch，不依赖图框架

Experimental graph learning framework that injects motif statistics from HiPerXplorer into GNNs (GCN, GraphSAGE, GAT) via concat, gate, and mix strategies. Built on PyTorch Geometric.

A multimodal product recommendation system integrating LLM with RAG and GraphSAGE neural networks. Leverages semantic search, graph-based product relationships, and fashion domain expertise for intelligent recommendations. Built with PyTorch, ChromaDB, and Streamlit for interactive product discovery.

A PyTorch implementation of the paper https://www-cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf

graphSAGE with pytorch

pytorch version of graphsage

GraphSAGE implemented in PyTorch.

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A PyTorch implementation of GraphSAGE