Dual-View Graph-of-Graph Representation Learning with Graph Transformer for Graph-Level Anomaly Detection

Information Science, Computer Science, Artificial Intelligence,
Graph-Level Anomaly Detection   Graph Transformer   Graph Neural Networks   Unsupervised Graph Representation Learning  

Research on Graph-Level Anomaly Detection Based on Dual-View Graph-of-Graph Representation Learning Academic Background In today’s data-driven world, graphs, as a powerful data structure, are widely used in fields such as social network analysis, financial fraud detection, and bioinformatics. Graphs can effectively represent complex relational data...

Hierarchical Negative Sampling Based Graph Contrastive Learning Approach for Drug-Disease Association Prediction

Information Science, Computer Science, Artificial Intelligence, Medicine, Pharmacy, Life Sciences, Biochemistry,
Drug-Disease Association   Contrastive Learning   Graph Neural Networks   Negative Sampling   Heterogeneous Network  

Research on Drug-Disease Association Prediction Using Graph Contrastive Learning Based on Layered Negative Sampling The prediction of drug-disease associations (RDAs) plays a critical role in unveiling disease treatment strategies and promoting drug repurposing. However, existing methods mainly rely on limited domain-specific knowledge when predict...

Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph

Information Science, Computer Science, Artificial Intelligence,
Recommendation Systems   Knowledge Graph   Graph Neural Networks   Contextual Information   Biased Random Walk   Recurrent Neural Network   Personalized Preferences  

Knowledge Graph-based Recommendation System: Contextualized Graph Attention Network In recent years, with the explosive growth of online information and content, recommendation systems have become increasingly important in various scenarios such as e-commerce websites and social media platforms. These systems typically aim to provide users with a l...

Structure Enhanced Prototypical Alignment for Unsupervised Cross-Domain Node Classification

Information Science, Automation, Artificial Intelligence,
Graph Neural Networks   Node Classification   Graph Domain Adaptation   Transfer Learning   Unsupervised Learning  

Structurally Enhanced Prototype Alignment for Unsupervised Cross-Domain Node Classification Introduction With the advancement of modern information technology, Graph Neural Networks (GNNs) have demonstrated significant success in handling complex network node classification tasks. However, one key challenge is the need for a large amount of high-qu...