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You're reading from  Hands-On Graph Neural Networks Using Python

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Published inApr 2023
PublisherPackt
ISBN-139781804617526
Edition1st Edition
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Maxime Labonne
Maxime Labonne
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Maxime Labonne

Maxime Labonne is currently a senior applied researcher at Airbus. He received a M.Sc. degree in computer science from INSA CVL, and a Ph.D. in machine learning and cyber security from the Polytechnic Institute of Paris. During his career, he worked on computer networks and the problem of representation learning, which led him to explore graph neural networks. He applied this knowledge to various industrial projects, including intrusion detection, satellite communications, quantum networks, and AI-powered aircrafts. He is now an active graph neural network evangelist through Twitter and his personal blog.
Read more about Maxime Labonne

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Predicting cases of COVID-19

This section will focus on a new application with epidemic forecasting. We will use the England Covid dataset, a dynamic graph with temporal information introduced by Panagopoulos et al. in 2021 [3]. While nodes are static, connections between and edge weights vary over time. This dataset represents the number of reported cases of COVID-19 in 129 England NUTS 3 regions between March 3 and May 12, 2020. Data was collected from mobile phones that installed the Facebook application and shared their location history. Our goal is to predict the number of cases in each node (region) in 1 day.

Figure 13.8 – NUTS 3 areas in England are colored in red

Figure 13.8 – NUTS 3 areas in England are colored in red

This dataset represents England as a graph . Due to the temporal nature of this dataset, it is composed of multiple graphs corresponding to each day of the studied period . In these graphs, node features correspond to the number of cases in each of the past days in this region...

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Hands-On Graph Neural Networks Using Python
Published in: Apr 2023Publisher: PacktISBN-13: 9781804617526
© 2023 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Maxime Labonne

Maxime Labonne is currently a senior applied researcher at Airbus. He received a M.Sc. degree in computer science from INSA CVL, and a Ph.D. in machine learning and cyber security from the Polytechnic Institute of Paris. During his career, he worked on computer networks and the problem of representation learning, which led him to explore graph neural networks. He applied this knowledge to various industrial projects, including intrusion detection, satellite communications, quantum networks, and AI-powered aircrafts. He is now an active graph neural network evangelist through Twitter and his personal blog.
Read more about Maxime Labonne

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