graph neural networks

Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure
Graph embedding has shown its effectiveness to represent graph information and capture deep relationships in graph data. Most recent …
OpenWGL: Open-World Graph Learning
In traditional graph learning tasks, such as node classification, the learning is carried out in a closed-world setting where the …
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Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including …
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Multivariate Relations Aggregation Learning in Social Networks
Multivariate relations are general in various types of networks, such as biological networks, social networks, transportation networks, …
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Hyperspectral Image Classification with Context-aware Dynamic Graph Convolutional Networks
In hyperspectral image (HSI) classification, spatial context has demonstrated its significance in achieving promising performance. …
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A comprehensive survey on graph neural networks
Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to …
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Unsupervised Domain Adaptive Graph Convolutional Networks
Graph convolutional networks (GCNs) have achieved impressive success in many graph related analytics tasks. However, most GCNs only …
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Going Deep: Graph Convolutional Ladder-shape Networks
Neighborhood aggregation algorithms like spectral graph convolutional networks (GCNs) formulate graph convolutions as a symmetric …
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GSSNN: Graph Smoothing Splines Neural Networks
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in many graph data analysis tasks. However, they still suffer …
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Domain-Adversarial Graph Neural Networks for Text Classification
Text classification, in cross-domain setting, is a challenging task. On the one hand, data from other domains are often useful to …
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