Official implementation of NeurIPS'23 paper "Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection"

Repository for the paper: 'Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models'

A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)

Pytorch implementation of various Graph Neural Networks (GNNs) for graph classification

Official Repository of "A Fair Comparison of Graph Neural Networks for Graph Classification", ICLR 2020

Official implement of RAHG: A Role-Aware Hypergraph Neural Network for Node Classification in Graphs.

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 t…

cogdl based GNN model for anomoly detection on Amazon & YelpChi datasets

A collection of GNN-based fake news detection models.

Multivariate Time Series Anomaly Detection with GNNs and Latent Graph Inference

Paper collection for graph based models in finance application

Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.

基于统计学的时间序列预测(AR,ARM).

PPO implementation of the DRL agent used in the paper "Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case"

FinRL®: Financial Reinforcement Learning. 🔥

Summary of open source code for deep learning models in the field of traffic prediction

R, ARMA, ARIMA, GARCH

A curated list of Graph/Transformer-based fraud, anomaly, and outlier detection papers & resources

List of papers, code and experiments using deep learning for time series forecasting

PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).

A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.

Build a LSTM encoder-decoder using PyTorch to make sequence-to-sequence prediction for time series data

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

attention-based LSTM/Dense implemented by Keras

Implementation of the paper "Adversarial Attacks on Graph Neural Networks via Meta Learning".

Implementation and experiments of graph neural netwokrs, like gcn,graphsage,gat,etc.

The implement of GNN based on Pytorch

Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet

LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data