Deep Learning models for dynamic graphs

• The development of Deep Learning models applicable for dynamic graphs respecting the variety of existing graph types and the associated difficulties,
  • Graph Neural Networks Designed for Different Graph Types: A Survey
  • A Note On The Modeling Power Of Different Graph Types
  
  
• The consideration of different dynamics w.r.t. the graph structure and attributes and consequent adaptations of established approaches,
  • Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Network
  • Using local activity encoding for dynamic graph pooling in stuctural-dynamic graphs
  • Continuous-time generative graph neural network for attributed dynamic graphs
  
  
• The examination of the expressivity of Deep Learning techniques on graphs,
  • Weisfeiler–Lehman goes dynamic: An analysis of the expressive power of Graph Neural Networks for attributed and dynamic graphs