Simple reference implementation of GraphSAGE.

Last update: Jan 06, 2023

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Overview

Reference PyTorch GraphSAGE Implementation

Author: William L. Hamilton

Basic reference PyTorch implementation of GraphSAGE. This reference implementation is not as fast as the TensorFlow version for large graphs, but the code is easier to read and it performs better (in terms of speed) on small-graph benchmarks. The code is also intended to be simpler, more extensible, and easier to work with than the TensorFlow version.

Currently, only supervised versions of GraphSAGE-mean and GraphSAGE-GCN are implemented.

Requirements

pytorch >0.2 is required.

Running examples

Execute python -m graphsage.model to run the Cora example. It assumes that CUDA is not being used, but modifying the run functions in model.py in the obvious way can change this. There is also a pubmed example (called via the run_pubmed function in model.py).

Owner

William L Hamilton

Assistant Professor at McGill University and Mila, working on machine learning, NLP, and network analysis.

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Simple reference implementation of GraphSAGE.

Related tags

Overview

Reference PyTorch GraphSAGE Implementation

Author: William L. Hamilton

Requirements

Running examples

Owner

William L Hamilton

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Simple reference implementation of GraphSAGE.

Related tags

Overview

Reference PyTorch GraphSAGE Implementation

Author: William L. Hamilton

Requirements

Running examples

Owner

William L Hamilton

Language-Agnostic Website Embedding and Classification

Lipschitz-constrained Unsupervised Skill Discovery

Pytorch implementation of "Neural Wireframe Renderer: Learning Wireframe to Image Translations"

Automatic library of congress classification, using word embeddings from book titles and synopses.

Graduation Project

Housing Price Prediction

A set of examples around hub for creating and processing datasets

Fuzzing the Kernel Using Unicornafl and AFL++

Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Context Terms

Paddle implementation for "Highly Efficient Knowledge Graph Embedding Learning with Closed-Form Orthogonal Procrustes Analysis" (NAACL 2021)

A general framework for deep learning experiments under PyTorch based on pytorch-lightning

banditml is a lightweight contextual bandit & reinforcement learning library designed to be used in production Python services.

Weakly Supervised End-to-End Learning (NeurIPS 2021)

SOTA model in CIFAR10

​ This is the Pytorch implementation of Progressive Attentional Manifold Alignment.

Recognize numbers from an (28 x 28) image using neural networks

Caffe-like explicit model constructor. C(onfig)Model

Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning

DeepMoCap: Deep Optical Motion Capture using multiple Depth Sensors and Retro-reflectors

A visualization tool to show a TensorFlow's graph like TensorBoard

This is the Pytorch implementation of Progressive Attentional Manifold Alignment.