Graph Neural Networks

Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs
Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and …
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Projective Ranking-based GNN Evasion Attacks
Graph neural networks (GNNs) offer promising learning methods for graph-related tasks. However, GNNs are at risk of adversarial …
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Reinforced, Incremental and Cross-lingual Event Detection From Social Messages
Detecting hot social events (e.g. political scandal, momentous meetings, natural hazards, etc.) from social messages iscrucial as it …
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Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming
Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks …
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Graph self-supervised learning: A survey
Deep learning on graphs has attracted significant interests recently. However, most of the works have focused on (semi-) supervised …
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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 …