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 …
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 …
Graph correlated attention recurrent neural network for multivariate time series forecasting
X Geng, X He, L Xu, J Yu - Information Sciences, 2022 - Elsevier
Multivariate time series (MTS) forecasting is an urgent problem for numerous valuable
applications. At present, attention-based methods can relieve recurrent neural networks' …
applications. At present, attention-based methods can relieve recurrent neural networks' …
Causal GraphSAGE: A robust graph method for classification based on causal sampling
T Zhang, HR Shan, MA Little - Pattern Recognition, 2022 - Elsevier
GraphSAGE is a widely-used graph neural network for classification, which generates node
embeddings in two steps: sampling and aggregation. In this paper, we introduce causal …
embeddings in two steps: sampling and aggregation. In this paper, we introduce causal …
On region-level travel demand forecasting using multi-task adaptive graph attention network
Accurate travel demand forecasting at the regional level benefits to urban traffic
management and service operations. Irregular regions can be naturally represented by …
management and service operations. Irregular regions can be naturally represented by …
[HTML][HTML] RT-GCN: Gaussian-based spatiotemporal graph convolutional network for robust traffic prediction
Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart
cities. Travelers as well as urban managers rely on reliable traffic information to make their …
cities. Travelers as well as urban managers rely on reliable traffic information to make their …
Accel-gcn: High-performance gpu accelerator design for graph convolution networks
Graph Convolutional Networks (GCNs) are pivotal in extracting latent information from graph
data across various domains, yet their acceleration on mainstream GPUs is challenged by …
data across various domains, yet their acceleration on mainstream GPUs is challenged by …
A rumor heat prediction model based on rumor and anti-rumor multiple messages and knowledge representation
In the field of online social networks, the effective prediction of group behavior is the key to
fitting the trajectory of rumor topic propagation. Considering the heterogeneity and …
fitting the trajectory of rumor topic propagation. Considering the heterogeneity and …
Time-series graph network for sea surface temperature prediction
Sea surface temperature (SST) is an important indicator for balancing surface energy and
measuring sea heat. Various effects caused by the sea temperature field significantly affect …
measuring sea heat. Various effects caused by the sea temperature field significantly affect …
GraphSAGE-based dynamic spatial–temporal graph convolutional network for traffic prediction
T Liu, A Jiang, J Zhou, M Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Traffic networks exhibit complex spatial-temporal dependencies, and accurately capturing
such dependencies is critical to improving prediction accuracy. Recently, many deep …
such dependencies is critical to improving prediction accuracy. Recently, many deep …