Official implementation of MAG-GNN: an RL-boosted graph learning framework.

[SIGIR'2024] "SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendation"

《深入浅出图神经网络：GNN原理解析》配套代码

Graph Neural Network Library for PyTorch

A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)

SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)

Must-read papers on graph neural networks (GNN)

Official repository of ICML 2023 paper - From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks

[IJCAI'18] Spatio-Temporal Graph Convolutional Networks

DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting, which is accepted at ICML2022.

[ICDE'2022] "Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction"

This is the repository for the collection of Graph Neural Network for Traffic Forecasting.

Implementation of Diffusion Convolutional Recurrent Neural Network in Tensorflow

AAAI 2020. Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting

This is a Pytorch implementation of ASTGNN. Now the corresponding paper is available online at https://ieeexplore.ieee.org/document/9346058.

Code of STFGNN@AAAI-2021 (Spatial-Temporal/ Traffic data forecasting)

Paper:Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting . Implementation of spatio-temporal graph convolutional network with PyTorch

Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting, AAAI 2019, pytorch version

Pedestrian Traffic Forecasting in Melbourne City Centre (Seasonal ARIMA / LSTM)

Time Series model using ARIMA to forecast passenger traffic on JetRail for Unicorn Investors

Implemented Airline Passengers Traffic Forecasting using ARIMA Model for next 5 years.

Forecasting future traffic to Wikipedia pages using AR MA ARIMA : Removing trend and seasonality with decomposition

TensorFlow implementation of 'Ask Me Anything: Dynamic Memory Networks for Natural Language Processing (2015)'

Implementation of Dynamic memory networks by Kumar et al. https://arxiv.org/abs/1506.07285

"End-To-End Memory Networks" in Tensorflow

“Key-Value Memory Networks for Directly Reading Documents”的tensorflow实现方案，使用的数据集是MovieQA