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Hands-On Graph Neural Networks Using Python

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

Product type Book
Published in Apr 2023
Publisher Packt
ISBN-13 9781804617526
Pages 354 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Maxime Labonne Maxime Labonne
Profile icon Maxime Labonne
LinkedIn
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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Table of Contents (25) Chapters

Preface Part 1: Introduction to Graph Learning
Chapter 1: Getting Started with Graph Learning Chapter 2: Graph Theory for Graph Neural Networks Chapter 3: Creating Node Representations with DeepWalk Part 2: Fundamentals
Chapter 4: Improving Embeddings with Biased Random Walks in Node2Vec Chapter 5: Including Node Features with Vanilla Neural Networks Chapter 6: Introducing Graph Convolutional Networks Chapter 7: Graph Attention Networks Part 3: Advanced Techniques
Chapter 8: Scaling Up Graph Neural Networks with GraphSAGE Chapter 9: Defining Expressiveness for Graph Classification Chapter 10: Predicting Links with Graph Neural Networks Chapter 11: Generating Graphs Using Graph Neural Networks Chapter 12: Learning from Heterogeneous Graphs Chapter 13: Temporal Graph Neural Networks Chapter 14: Explaining Graph Neural Networks Part 4: Applications
Chapter 15: Forecasting Traffic Using A3T-GCN Chapter 16: Detecting Anomalies Using Heterogeneous GNNs Chapter 17: Building a Recommender System Using LightGCN Chapter 18: Unlocking the Potential of Graph Neural Networks for Real-World Applications
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Further reading

  • [1] Weisfeiler and Lehman, A.A. (1968) A Reduction of a Graph to a Canonical Form and an Algebra Arising during This Reduction. Nauchno-Technicheskaya Informatsia, 9.
  • [2] K. Xu, W. Hu, J. Leskovec, and S. Jegelka, How Powerful are Graph Neural Networks? arXiv, 2018. doi: 10.48550/ARXIV.1810.00826.
  • [3] C. Morris et al., Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks. arXiv, 2018. doi: 10.48550/ARXIV.1810.02244.
  • [4] V. P. Dwivedi et al. Benchmarking graph neural networks. arXiv, 2020. doi: 10.48550/ARXIV.2003.00982.
  • [5] K. M. Borgwardt, C. S. Ong, S. Schoenauer, S. V. N. Vishwanathan, A. J. Smola, and H. P. Kriegel. Protein function prediction via graph kernels. Bioinformatics, 21(Suppl 1):i47–i56, Jun 2005.
  • [6] P. D. Dobson and A. J. Doig. Distinguishing enzyme structures from non-enzymes without alignments. J. Mol. Biol., 330(4):771–783, Jul 2003.
  • [7] Christopher Morris and Nils M. Kriege and Franka Bause...
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Authors (1)

Profile icon Maxime Labonne
LinkedIn
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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See other products by Maxime Labonne

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