The evolution of distributed systems for graph neural networks and their origin in graph processing and deep learning: A survey
Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …
neural network architecture is capable of processing graph structured data and bridges the …
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 …
The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study
Deep learning (DL)-based methods have advanced the field of Prognostics and Health
Management (PHM) in recent years, because of their powerful feature representation ability …
Management (PHM) in recent years, because of their powerful feature representation ability …
ROLAND: graph learning framework for dynamic graphs
Graph Neural Networks (GNNs) have been successfully applied to many real-world static
graphs. However, the success of static graphs has not fully translated to dynamic graphs due …
graphs. However, the success of static graphs has not fully translated to dynamic graphs due …
Long sequence time-series forecasting with deep learning: A survey
The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …
critical problem globally, resulting in negative consequences such as lost hours of additional …
Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient
G Lin, A Lin, D Gu - Information Sciences, 2022 - Elsevier
The prediction of short-term traffic flow is critical for improving service levels for drivers and
passengers as well as enhancing the efficiency of traffic management in the urban …
passengers as well as enhancing the efficiency of traffic management in the urban …
Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning
Exploiting deep learning techniques for traffic flow prediction has become increasingly
widespread. Most existing studies combine CNN or GCN with recurrent neural network to …
widespread. Most existing studies combine CNN or GCN with recurrent neural network to …
Dynamic network embedding survey
Since many real world networks are evolving over time, such as social networks and user-
item networks, there are increasing research efforts on dynamic network embedding in …
item networks, there are increasing research efforts on dynamic network embedding in …
Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks
For intelligent transportation systems (ITS), predicting urban traffic crowd flows is of great
importance. However, it is challenging to represent various complex spatial relationships …
importance. However, it is challenging to represent various complex spatial relationships …