dhuQChen's starred repositories
TAM-master
Official implementation of NeurIPS'23 paper "Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection"
graph-neural-networks-for-graph-classification
Pytorch implementation of various Graph Neural Networks (GNNs) for graph classification
gnn-comparison
Official Repository of "A Fair Comparison of Graph Neural Networks for Graph Classification", ICLR 2020
GNNs-for-Node-Classification
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 the future.
anomoly_detection_cogdl
cogdl based GNN model for anomoly detection on Amazon & YelpChi datasets
GNN-FakeNews
A collection of GNN-based fake news detection models.
mulivariate-time-series-anomaly-detection
Multivariate Time Series Anomaly Detection with GNNs and Latent Graph Inference
Graph-models-in-finance-application
Paper collection for graph based models in finance application
machine-learning-for-trading
Code for Machine Learning for Algorithmic Trading, 2nd edition.
TimeSeriesForecasting
基于统计学的时间序列预测(AR,ARM).
DRL-GNN-PPO
PPO implementation of the DRL agent used in the paper "Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case"
Traffic-Prediction-Open-Code-Summary
Summary of open source code for deep learning models in the field of traffic prediction
Time-Series
R, ARMA, ARIMA, GARCH
graph-fraud-detection-papers
A curated list of graph-based fraud, anomaly, and outlier detection papers & resources
deep-learning-time-series
List of papers, code and experiments using deep learning for time series forecasting
mtad-gat-pytorch
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).
GNNs-in-Network-Neuroscience
A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.
LSTM_encoder_decoder
Build a LSTM encoder-decoder using PyTorch to make sequence-to-sequence prediction for time series data
pytorch-seq2seq
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
LSTM_Attention
attention-based LSTM/Dense implemented by Keras
gnn-meta-attack
Implementation of the paper "Adversarial Attacks on Graph Neural Networks via Meta Learning".
GraphNeuralNetwork
Implementation and experiments of graph neural netwokrs, like gcn,graphsage,gat,etc.
PyTorch-GNNs
The implement of GNN based on Pytorch
Unet-Segmentation-Pytorch-Nest-of-Unets
Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet
LSTM-Neural-Network-for-Time-Series-Prediction
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data