Deep Learning for Graphs

Tutorial @ WCCI 2020

Sunday, 19th July 2020 - Scottish Event Campus (SEC) - Glasgow (UK)

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.

Bibliography

D. Bacciu, F.Errica, A. Micheli, M. Podda, A Gentle Introduction to Deep Learning for Graphs, Arxiv, 2020

Presenter Biography

Davide Bacciu - UniversitĂ  di Pisa (bacciu@di.unipi.it)

Assistant Professor at the Computer Science Department, University of Pisa. The core of his research is on Machine Learning (ML) and deep learning models for structured data processing, including sequences, trees and graphs. He is the PI of an Italian National project on ML for structured data and the Coordinator of the H2020-RIA project TEACHING (2020-2022). He has been teaching courses of Artificial Intelligence (AI) and ML at undergraduate and graduate levels since 2010. He is currently supervising 13 Ph.D. students in Computer Science and Data Science, including 5 Ph.D. students whose main focus is on learning for structured data. He is an IEEE Senior Member, the founder and chair of the IEEE Task Force on learning for structured data, a member of the IEEE NN Technical Committee and of the IEEE CIS Task Force on Deep Learning. He is an Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems. Since 2017 he is the Secretary of the Italian Association for Artificial Intelligence (AI*IA).

Site under development. More information out soon.