Deep Learning for Graphs

Tutorial @ IJCNN 2021

18-22 July 2021 - Online Event

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.

Materials

Tutorial slides are will be made available here before the event.

Tutorial paper on Deep Learning for graphs:

D. Bacciu, F.Errica, A. Micheli, M. Podda, A Gentle Introduction to Deep Learning for Graphs, Neural Networks, Vol 129, pp 203-221, 2020

Presenter Biography

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

Associate 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, the Coordinator of the H2020-RIA project TEACHING (2020-2022) and the local PI for the H2020 TAILOR project (2020-2023). He has been teaching courses of Artificial Intelligence (AI) and ML at undergraduate and graduate levels since 2010. He is currently supervising 18 Ph.D. students in Computer Science and Data Science, including 9 Ph.D. students whose main focus is on learning for structured data. He is the founder and co-coordinator of the joint CNR-UNIPI laboratory on Pervasive AI.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).

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