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Spatio-Temporal Sparsification for General Robust Graph Convolution Networks
Graph Neural Networks (GNNs) have attracted increasing attention due to its successful applications on various graph-structure data.
Urban Traffic Flow Forecast Based on FastGCRNN
Traffic forecasting is an important prerequisite for the application of intelligent transportation systems in urban traffic networks.
Graph Hierarchical Convolutional Recurrent Neural Network (GHCRNN) for Vehicle Condition Prediction
Most of the existing research methods are to extract spatial structure information on the road network and extract time series information from the historical data.
Interpreting and Understanding Graph Convolutional Neural Network using Gradient-based Attribution Method
To solve the problem that convolutional neural networks (CNNs) are difficult to process non-grid type relational data like graphs, Kipf et al. proposed a graph convolutional neural network (GCN).
Program Classification Using Gated Graph Attention Neural Network for Online Programming Service
The online programing services, such as Github, TopCoder, and EduCoder, have promoted a lot of social interactions among the service users.
Based on Graph-VAE Model to Predict Student's Score
In this paper, the graph neural network matrix filling model (Graph-VAE) based on deep learning can automatically extract features without a large amount of prior knowledge.
