graph neural networks

CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning
Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic …
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Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications. However, with the …
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Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realisation
Machine learning models are shown to face a severe threat from Model Extraction Attacks, where a well-trained private model owned by a …
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Predicting Best-Selling New Products in a Major Promotion Campaign through Graph Convolutional Networks
Many e-commerce platforms, such as AliExpress, run major promotion campaigns regularly. Before such a promotion, it is important to …
Attraction and Repulsion: Unsupervised Domain Adaptive Graph Contrastive Learning Network
Graph convolutional networks (GCNs) are important techniques for many graph related analytics tasks. To date, most GCNs are designed …
GCNFusion: An efficient graph convolutional network based model for information diffusion
Investigating the dynamics of spreading processes in real-world applications such as pathogen spread prediction, marketing, political …
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Dual Space Graph Contrastive Learning
Unsupervised graph representation learning has emerged as a powerful tool to address real-world problems and achieves huge success in …
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Towards Unsupervised Deep Graph Structure Learning
In recent years, graph neural networks (GNNs) have emerged as a successful tool in a variety of graph-related applications. However, …
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Anomaly Detection in Dynamic Graphs via Transformer
Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks,e-commerce, and …
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Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels
Graph Neural Networks (GNNs) have achieved remarkable performance in the task of semi-supervised node classification. However, most …
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