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

Product typeBook
Published inApr 2023
PublisherPackt
ISBN-139781804617526
Edition1st Edition
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Author (1)
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.
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Generating graphs with traditional techniques

Traditional graph generation techniques have been studied for decades. This is why they are well understood and can be used as baselines in various applications. However, they are often limited in the type of graphs they can generate. Most of them are specialized to output certain topologies, which is why they cannot simply imitate a given network.

In this section, we will introduce two classical techniques: the Erdős–Rényi and the small-world models.

The Erdős–Rényi model

The Erdős–Rényi model is the simplest and most popular random graph model. It was introduced by Hungarian mathematicians Paul Erdős and Alfréd Rényi in 1959 [1] and was independently proposed by Edgar Gilbert the same year [2]. This model has two variants: and .

The model is straightforward: we are given nodes and a probability of connecting a pair of nodes. We try to randomly connect...

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Hands-On Graph Neural Networks Using Python
Published in: Apr 2023Publisher: PacktISBN-13: 9781804617526

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