Graph Neural Networks

Learning Graph Neural Networks with Positive and Unlabeled Nodes
Graph neural networks (GNNs) are important tools for transductive learning tasks, such as node classification in graphs, due to their …
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Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning
Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled …
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Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement
Recent works on One-Shot Neural Architecture Search (NAS) mostly adopt a bilevel optimization scheme to alternatively optimize the …
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Graph Geometry Interaction Learning
While numerous approaches have been developed to embed graphs into either Euclidean or hyperbolic spaces, they do not fully utilize the …
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Graph Stochastic Neural Networks for Semi-supervised Learning
Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification. However, most …
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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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