Skip to main content
  • CIS
    Members: Free
    IEEE Members: Free
    Non-members: Free
    Length: 01:37:04
19 Jul 2020

The tutorial will introduce the lively field of deep learning for graphs and its applications. Dealing with graph data requires learning models capable of adapting to structured samples of varying size and topology, capturing the relevant structural patterns to perform predictive and explorative tasks while maintaining the efficiency and scalability necessary to process large scale networks. The tutorial will first introduce foundational aspects and seminal models for learning with graph structured data. Then it will discuss the most recent advancements in terms of deep learning for network and graph data, including learning structure embeddings, graph convolutions, attentional models and graph generation.

More Like This

  • CIS
    Members: Free
    IEEE Members: Free
    Non-members: Free
  • CIS
    Members: Free
    IEEE Members: Free
    Non-members: Free