You're reading from Hands-On Graph Neural Networks Using Python Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch

Product type Paperback

Published in Apr 2023

Publisher Packt

ISBN-13 9781804617526

Length 354 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Deep Learning

Author (1):

Maxime Labonne

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Table of Contents (25) Chapters

Preface

1. Part 1: Introduction to Graph Learning

2. Chapter 1: Getting Started with Graph Learning FREE CHAPTER

3. Chapter 2: Graph Theory for Graph Neural Networks

4. Chapter 3: Creating Node Representations with DeepWalk

5. Part 2: Fundamentals

6. Chapter 4: Improving Embeddings with Biased Random Walks in Node2Vec

7. Chapter 5: Including Node Features with Vanilla Neural Networks

8. Chapter 6: Introducing Graph Convolutional Networks

9. Chapter 7: Graph Attention Networks

10. Part 3: Advanced Techniques

11. Chapter 8: Scaling Up Graph Neural Networks with GraphSAGE

12. Chapter 9: Defining Expressiveness for Graph Classification

13. Chapter 10: Predicting Links with Graph Neural Networks

14. Chapter 11: Generating Graphs Using Graph Neural Networks

15. Chapter 12: Learning from Heterogeneous Graphs

16. Chapter 13: Temporal Graph Neural Networks

17. Chapter 14: Explaining Graph Neural Networks

18. Part 4: Applications

19. Chapter 15: Forecasting Traffic Using A3T-GCN

20. Chapter 16: Detecting Anomalies Using Heterogeneous GNNs

21. Chapter 17: Building a Recommender System Using LightGCN

22. Chapter 18: Unlocking the Potential of Graph Neural Networks for Real-World Applications

23. Index

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24. Other Books You May Enjoy

Exploring the CIDDS-001 dataset

This section will explore the dataset and get more insights about feature importance and scaling.

The CIDDS-001 dataset [1] is designed to train and evaluate anomaly-based network intrusion detection systems. It provides realistic traffic that includes up-to-date attacks to assess these systems. It was created by collecting and labeling 8,451,520 traffic flows in a virtual environment using OpenStack. Precisely, each row corresponds to a NetFlow connection, describing Internet Protocol (IP) traffic statistics, such as the number of bytes exchanged.

The following figure provides an overview of the simulated network environment in CIDDS-001.

Figure 16.1 – Overview of the virtual network simulated by CIDDS-001

We see four different subnets (developer, office, management, and server) with their respective IP address ranges. All these subnets are linked to a single server connected to the internet through a firewall...

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You're reading from Hands-On Graph Neural Networks Using Python Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch

Table of Contents (25) Chapters

Exploring the CIDDS-001 dataset

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Table of Contents (25) Chapters

Exploring the CIDDS-001 dataset

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Authors (1)

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