DySAT
Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, Hao Yang
Abstract
Learning node representations in graphs is important for many applications such as link prediction, node classification, and community detection. Existing graph representation learning methods primarily target static graphs while many real-world graphs evolve over time. Complex time-varying graph structures make it challenging to learn informative node representations over time.
Topics & Concepts
Computer scienceNode (physics)GraphTheoretical computer scienceRepresentation (politics)Artificial intelligenceStructural engineeringLawPoliticsPolitical scienceEngineeringAdvanced Graph Neural NetworksComplex Network Analysis TechniquesData Quality and Management