Investigating_graphnn

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Разработки моей группы

Рекомендую обратить внимание на Сверточная нейронная сеть на графах с быстрой локализованной спектральной фильтрацией на основе модели LeNet5

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Прочее

Get started with igraph in Python

JuliaGraphsTutorials

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)]

PyG (PyTorch Geometric)

Graph Representation Learning

Awesome Graph Classification

StellarGraph Machine Learning Library

GraphWorld

Basic

Basics of Graph Neural Networks

An Illustrated Guide to Graph Neural Networks

Graph Neural Networks — Introduction

How to get started with Graph Machine Learning

Exploiting Graph Structure to Learn Into an Elegant Semi-Supervised Way : The Label Propagation Algorithm.

Understanding Graph Convolutional Networks for Node Classification

How to do Deep Learning on Graphs with Graph Convolutional Networks - 1

How to do Deep Learning on Graphs with Graph Convolutional Networks - 2

Graph Convolutional Networks (GCN)

GNN for MNIST

Getting the Intuition of Graph Neural Networks

Understanding Graph Neural Network with hands-on example| Part-1

Understanding Graph Neural Network with hands-on example| Part-2

• MNIST_Superpixel_GCN.ipynb

MNIST Graph Deep Learning

Tutorial on Graph Neural Networks for Computer Vision and Beyond

• 2D Convolution using ajacency matrix

Karate Club

How to Do Deep Learning on Graphs with Graph Convolutional Networks

learn_graph_ml

Graph Neural Networks: A learning journey since 2008— Part 1

Graph Neural Networks: A learning journey since 2008 — Part 2

Graph Neural Networks: A learning journey since 2008 — Deep Walk

Graph Neural Networks: a learning journey since 2008 — Python & Deep Walk

Graph Neural Networks: A learning journey since 2008 — Graph Convolutional Network

Graph Neural Networks: A Learning Journey since 2008 — Python & Graph Convolutional Network

Cora dataset

Graph_Convolutional_Networks_Node_Classification

PPIDataset

PyTorch Geometric vs Deep Graph Library

Time Series Forecasting with Graph Convolutional Neural Network

Time Series Forecasting with Graph Convolutional Neural Network

Traffic forecasting using graph neural networks and LSTM

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SPECTRAL (Examples)

This is a collection of examples that you can use as template for your projects.

Node-level prediction

• Citation networks with GCN
• Citation networks with GCN (custom training loop)
• Citation networks with ChebConv
• Citation networks with GAT
• Citation networks with GAT (custom training loop)
• Citation networks with ARMA
• Citation networks with SimpleGCN (custom transform)
• Open Graph Benchmark dataset

Graph-level prediction

• General GNN
• Custom dataset
• OGB mol-hiv classification (edge attributes)
• QM9 regression with ECC (custom training loop)
• QM9 regression with ECC (batch mode)
• TUDataset classification with GIN
• TUDataset classification with MinCut pooling

Other applications

• Graph signal classification on MNIST (mixed mode)
• Node clustering on citation networks with MinCut pooling (unsupervised)
• Link prediction with Variational Graph Autoencoders (by Filippo Bianchi)
• Kernel Graph Convolutional Neural Networks
• Awesome Graph Classification
• Multi-Task Graph Autoencoders