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Graph Machine Learning

You're reading from Graph Machine Learning Take graph data to the next level by applying machine learning techniques and algorithms

Product type Paperback

Published in Jun 2021

Publisher Packt

ISBN-13 9781800204492

Length 338 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Authors (3):

Aldo Marzullo

Claudio Stamile

Enrico Deusebio

View More author details

Table of Contents (15) Chapters

Preface

1. Section 1 – Introduction to Graph Machine Learning

2. Chapter 1: Getting Started with Graphs FREE CHAPTER

3. Chapter 2: Graph Machine Learning

4. Section 2 – Machine Learning on Graphs

5. Chapter 3: Unsupervised Graph Learning

6. Chapter 4: Supervised Graph Learning

7. Chapter 5: Problems with Machine Learning on Graphs

8. Section 3 – Advanced Applications of Graph Machine Learning

9. Chapter 6: Social Network Graphs

10. Chapter 7: Text Analytics and Natural Language Processing Using Graphs

11. Chapter 8:Graph Analysis for Credit Card Transactions

12. Chapter 9: Building a Data-Driven Graph-Powered Application

13. Chapter 10: Novel Trends on Graphs

14. Other Books You May Enjoy

Technical requirements

We will be using Jupyter notebooks with Python 3.8 for all of our exercises. The following is a list of the Python libraries that need to be installed for this chapter using pip. For example, run pip install networkx==2.5 on the command line:

Jupyter==1.0.0
networkx==2.5
scikit-learn==0.24.0 
numpy==1.19.2 
node2vec==0.3.3 
tensorflow==2.4.1 
stellargraph==1.2.1
communities==2.2.0 
git+https://github.com/palash1992/GEM.git

In the rest of this chapter, if not clearly stated, we will refer to nx, pd, and np as results of the following Python commands: import networkx as nx, import pandas as pd, and import numpy as np.

All code files relevant to this chapter are available at https://github.com/PacktPublishing/Graph-Machine-Learning/tree/main/Chapter06.

The rest of the chapter is locked

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Authors (3)

Claudio Stamile

Claudio Stamile

Claudio Stamile received an M.Sc. degree in computer science from the University of Calabria (Cosenza, Italy) in September 2013 and, in September 2017, he received his joint Ph.D. from KU Leuven (Leuven, Belgium) and Université Claude Bernard Lyon 1 (Lyon, France). During his career, he has developed a solid background in artificial intelligence, graph theory, and machine learning, with a focus on the biomedical field. He is currently a senior data scientist in CGnal, a consulting firm fully committed to helping its top-tier clients implement data-driven strategies and build AI-powered solutions to promote efficiency and support new business models.

See other products by Claudio Stamile

Enrico Deusebio

Enrico Deusebio

Enrico Deusebio is currently the chief operating officer at CGnal, a consulting firm that helps its top-tier clients implement data-driven strategies and build AI-powered solutions. He has been working with data and large-scale simulations using high-performance facilities and large-scale computing centers for over 10 years, both in an academic and industrial context. He has collaborated and worked with top-tier universities, such as the University of Cambridge, the University of Turin, and the Royal Institute of Technology (KTH) in Stockholm, where he obtained a Ph.D. in 2014. He also holds B.Sc. and M.Sc. degrees in aerospace engineering from Politecnico di Torino.

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Aldo Marzullo

Aldo Marzullo

Aldo Marzullo received an M.Sc. degree in computer science from the University of Calabria (Cosenza, Italy) in September 2016. During his studies, he developed a solid background in several areas, including algorithm design, graph theory, and machine learning. In January 2020, he received his joint Ph.D. from the University of Calabria and Université Claude Bernard Lyon 1 (Lyon, France), with a thesis entitled Deep Learning and Graph Theory for Brain Connectivity Analysis in Multiple Sclerosis. He is currently a postdoctoral researcher at the University of Calabria and collaborates with several international institutions.

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