We have developed two models: a novel 12-layer graph convolutional networks (GCNs) model and a hybrid GCN-LSTM for classifying EEG signals of schizophrenic subjects and healthy control by constructing graphs.

This repository provides the implementation of MVTS-based Solar Flare Prediction using GCN and LSTM. The repository is organised as follows: data/ contains the necessary datasets. models/ contains the implementation of the GCN-LSTM models as well as other baseline implementation.

The GCN_LSTM model in StellarGraph emulates the model as explained in the paper while giving additional flexibility of adding any number of graph convolution and LSTM layers. Concretely, the...

Inspired by the great success of deep learning frameworks, especially the convolution neural network (CNN) and long short-term memory (LSTM) network, we propose a novel end-to-end model with a Graph Convolution Network(GCN) embedded LSTM, named GC-LSTM, for dynamic network link prediction.

Contribute to qetmes/GCN-LSTM development by creating an account on GitHub.

We propose a deep-learning architecture combined residual network (ResNet), graph convolutional network (GCN) and long short-term memory (LSTM) (called “ResLSTM”) to forecast short-term passenger flow in urban rail transit on a network scale. First, improved methodologies of ResNet, GCN, and attention LSTM models are presented.

Inspired by the great success of deep learning frameworks, especially the convolution neural network (CNN) and long short-term memory (LSTM) network, we propose a novel end-to-end model with a Graph Convolution Network(GCN) embedded LSTM, named GC-LSTM, for dynamic network link prediction.

2018年12月11日 · Aiming at low AUC, high Error Rate, add/remove link prediction difficulty, we propose GC-LSTM, a Graph Convolution Network (GC) embedded Long Short Term Memory network (LTSM), for end-to-end dynamic link prediction.

2025年3月18日 · Optimizing Group Activity Recognition with Actor Relation Graphs and GCN-LSTM Architectures Abstract: The challenge of understanding and recognizing group activities through human behavior and interactions is a prominent issue in the realm of computer vision research. This area boasts a wide range of applications, including security ...

2022年5月1日 · Inspired by the great success of deep learning frameworks, especially the convolution neural network (CNN) and long short-term memory (LSTM) network, we propose a novel end-to-end model with a Graph Convolution Network (GCN) embedded LSTM, named GC-LSTM, for dynamic network link prediction.