Graph neural networks: Taxonomy, advances, and trends
Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-
dimensional spaces according to specific tasks. Up to now, there have been several surveys …
dimensional spaces according to specific tasks. Up to now, there have been several surveys …
Gated graph recurrent neural networks
Graph processes exhibit a temporal structure determined by the sequence index and and a
spatial structure determined by the graph support. To learn from graph processes, an …
spatial structure determined by the graph support. To learn from graph processes, an …
Spatio-temporal graph convolutional networks via view fusion for trajectory data analytics
Trajectory data contains rich spatial and temporal information. Turning trajectories into
graphs and then analyzing them efficiently in an AI-empowered way is a representative …
graphs and then analyzing them efficiently in an AI-empowered way is a representative …
Graph neural network for robust public transit demand prediction
Understanding and forecasting mobility patterns and travel demand are fundamental and
critical to efficient transport infrastructure planning and service operation. However, most …
critical to efficient transport infrastructure planning and service operation. However, most …
Adaptive graph convolutional collaboration networks for semi-supervised classification
Graph convolution networks (GCNs) have achieved remarkable success in processing non-
Euclidean data. GCNs update the feature representations of each sample by aggregating …
Euclidean data. GCNs update the feature representations of each sample by aggregating …
Text semantic fusion relation graph reasoning for few-shot object detection on remote sensing images
Most object detection methods based on remote sensing images are generally dependent
on a large amount of high-quality labeled training data. However, due to the slow acquisition …
on a large amount of high-quality labeled training data. However, due to the slow acquisition …
A practical tutorial on graph neural networks
Graph neural networks (GNNs) have recently grown in popularity in the field of artificial
intelligence (AI) due to their unique ability to ingest relatively unstructured data types as …
intelligence (AI) due to their unique ability to ingest relatively unstructured data types as …
Interference recommendation for the pump sizing process in progressive cavity pumps using graph neural networks
Pump sizing is the process of dimensional matching of an impeller and stator to provide a
satisfactory performance test result and good service life during the operation of progressive …
satisfactory performance test result and good service life during the operation of progressive …
Mathematical word problem generation from commonsense knowledge graph and equations
There is an increasing interest in the use of mathematical word problem (MWP) generation
in educational assessment. Different from standard natural question generation, MWP …
in educational assessment. Different from standard natural question generation, MWP …
Hypergraph representation for detecting 3D objects from noisy point clouds
P Jiang, X Deng, L Wang, Z Chen… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
It is challenging to detect 3D objects from noise point clouds by Graph Neural Networks
(GNNs), though graph-based methods have shown promising results in 3D classifications …
(GNNs), though graph-based methods have shown promising results in 3D classifications …