Graph Neural Networks

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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Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications
In light of the wide application of Graph Neural Networks (GNNs), Membership Inference Attack (MIA) against GNNs raises severe privacy …
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Hypergraph Convolutional Network for Group Recommendation
Group activities have become an essential part of people’s daily life, which stimulates the requirement for intensive research on the …
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Learning Graph Representations with Maximal Cliques
Non-Euclidean property of graph structures has faced interesting challenges when deep learning methods are applied. Graph convolutional …
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ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning
Anomaly detection on graphs plays a significant role in various domains, including cybersecurity, e-commerce, and financial fraud …
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Projective Ranking: A Transferable Evasion Attack Method on Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as a series of effective learning methods for graph-related tasks. However, GNNs are shown …
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