Deep learning with graph convolutional networks: An overview and latest applications in computational intelligence
Convolutional neural networks (CNNs) have received widespread attention due to their
powerful modeling capabilities and have been successfully applied in natural language …
powerful modeling capabilities and have been successfully applied in natural language …
A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
How attentive are graph attention networks?
Graph Attention Networks (GATs) are one of the most popular GNN architectures and are
considered as the state-of-the-art architecture for representation learning with graphs. In …
considered as the state-of-the-art architecture for representation learning with graphs. In …
Graph neural network for traffic forecasting: A survey
Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …
learning models, including convolution neural networks and recurrent neural networks, have …
Spatial-temporal fusion graph neural networks for traffic flow forecasting
Spatial-temporal data forecasting of traffic flow is a challenging task because of complicated
spatial dependencies and dynamical trends of temporal pattern between different roads …
spatial dependencies and dynamical trends of temporal pattern between different roads …
Graph learning: A survey
Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …
data. Graph data can be found in a broad spectrum of application domains such as social …
Decoupled dynamic spatial-temporal graph neural network for traffic forecasting
We all depend on mobility, and vehicular transportation affects the daily lives of most of us.
Thus, the ability to forecast the state of traffic in a road network is an important functionality …
Thus, the ability to forecast the state of traffic in a road network is an important functionality …
Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
YL Liao, T Smidt - arXiv preprint arXiv:2206.11990, 2022 - arxiv.org
Despite their widespread success in various domains, Transformer networks have yet to
perform well across datasets in the domain of 3D atomistic graphs such as molecules even …
perform well across datasets in the domain of 3D atomistic graphs such as molecules even …
Masked label prediction: Unified message passing model for semi-supervised classification
Graph neural network (GNN) and label propagation algorithm (LPA) are both message
passing algorithms, which have achieved superior performance in semi-supervised …
passing algorithms, which have achieved superior performance in semi-supervised …