Graph Neural Networks: Operators and Architectures

Staff - Faculty of Informatics

Date: 10 December 2021 / 14:30 - 16:00

USI Campus EST, room C1.04, Sector C // online on MS Teams

You are cordially invited to attend the PhD Dissertation Defence of Daniele Grattarola on Friday 10 December 2021 at 14:30 

You can join here

Abstract:
This thesis explores the field of graph neural networks, a class of deep learning models designed to learn representations of graphs. We organise the work into two parts. In the first part, we focus on the essential building blocks of graph neural networks. We present three novel operators for learning graph representations: one graph convolutional layer and two methods for pooling. We put particular emphasis on the topic of pooling, introducing a universal and modular framework to describe pooling operators, a taxonomy to organise the literature, and a set of evaluation criteria to assess an operator's performance. The second part focuses on specific graph neural network architectures and their applications to cutting-edge problems in dynamical systems and computational biology. We present three main contributions. First, we introduce an autoencoder architecture for learning graph representations in non-Euclidean spaces. We apply our model to the tasks of molecule generation and change detection in graph sequences. Second, we propose a graph neural network designed to be interpretable, specifically to solve the problem of seizure localisation in subjects with epilepsy. Finally, we discuss the design of autoregressive models for sequences of graphs.

Dissertation Committee:
- Prof. Cesare Alippi, Università della Svizzera italiana, Switzerland (Research Advisor)
- Prof. Lorenzo Livi, University of Manitoba, Canada (Research co-Advisor)
- Prof. Fabio Crestani, Università della Svizzera italiana, Switzerland (Internal Member)
- Prof. Luca Maria Gambardella, Università della Svizzera italiana, Switzerland (Internal Member)
- Prof. Plamen Angelov, Lancaster University, UK (External Member)
- Prof. Christos Panayiotou, University of Cyprus, Cyprus (External Member)
- Prof. Alessandro Sperduti, Università di Padova, Italy (External Member)

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