Top 10 Graph Machine Learning Books for Beginners

Are you interested in learning about graph machine learning? Do you want to know the best books to get started? Look no further! In this article, we will be discussing the top 10 graph machine learning books for beginners.

But first, let's define what graph machine learning is. Graph machine learning is a subfield of machine learning that deals with data represented as graphs. Graphs are a powerful way to represent complex relationships between entities, and graph machine learning algorithms can be used to make predictions or classify data based on these relationships.

Now, without further ado, let's dive into the top 10 graph machine learning books for beginners!

1. "Graph-Based Semi-Supervised Learning" by Xiaojun Chang, et al.

This book is a great introduction to graph-based semi-supervised learning, which is a popular technique in graph machine learning. The book covers the basics of graph theory, as well as the different types of graph-based semi-supervised learning algorithms. It also includes practical examples and code snippets to help you get started.

2. "Graph Convolutional Networks" by Thomas Kipf and Max Welling

This book is a comprehensive guide to graph convolutional networks (GCNs), which are a type of neural network designed for graph data. The book covers the basics of GCNs, as well as more advanced topics such as graph attention networks and graph autoencoders. It also includes practical examples and code snippets to help you implement GCNs in your own projects.

3. "Graph Algorithms" by Shashi Shekhar and Haifeng Li

This book is a great resource for learning about graph algorithms, which are essential for many graph machine learning tasks. The book covers a wide range of graph algorithms, including shortest path algorithms, spanning tree algorithms, and clustering algorithms. It also includes practical examples and code snippets to help you implement these algorithms in your own projects.

4. "Graph Databases" by Ian Robinson, Jim Webber, and Emil Eifrem

This book is a comprehensive guide to graph databases, which are a powerful tool for storing and querying graph data. The book covers the basics of graph databases, as well as more advanced topics such as graph modeling and graph query languages. It also includes practical examples and code snippets to help you get started with graph databases.

5. "Graph-Based Natural Language Processing and Information Retrieval" by Rada Mihalcea and Dragomir Radev

This book is a great resource for learning about graph-based natural language processing (NLP) and information retrieval (IR). The book covers the basics of graph-based NLP and IR, as well as more advanced topics such as graph-based summarization and question answering. It also includes practical examples and code snippets to help you implement these techniques in your own projects.

6. "Graph Theory and Its Applications" by Jonathan L. Gross and Jay Yellen

This book is a comprehensive guide to graph theory, which is the foundation of graph machine learning. The book covers the basics of graph theory, as well as more advanced topics such as planar graphs and graph coloring. It also includes practical examples and code snippets to help you understand the concepts.

7. "Mining of Massive Datasets" by Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman

This book is a great resource for learning about large-scale data mining, which is essential for many graph machine learning tasks. The book covers the basics of data mining, as well as more advanced topics such as clustering and recommendation systems. It also includes practical examples and code snippets to help you implement these techniques in your own projects.

8. "Networks, Crowds, and Markets" by David Easley and Jon Kleinberg

This book is a comprehensive guide to network science, which is the study of complex networks such as social networks and the internet. The book covers the basics of network science, as well as more advanced topics such as network dynamics and game theory. It also includes practical examples and code snippets to help you understand the concepts.

9. "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili

This book is a great resource for learning about machine learning in Python, which is a popular programming language for data science. The book covers the basics of machine learning, as well as more advanced topics such as deep learning and reinforcement learning. It also includes practical examples and code snippets to help you implement machine learning algorithms in Python.

10. "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

This book is a comprehensive guide to statistical learning, which is the foundation of machine learning. The book covers the basics of statistical learning, as well as more advanced topics such as regularization and ensemble methods. It also includes practical examples and code snippets to help you understand the concepts.

In conclusion, these are the top 10 graph machine learning books for beginners. Whether you are interested in graph-based semi-supervised learning, graph convolutional networks, graph algorithms, graph databases, graph-based NLP and IR, graph theory, large-scale data mining, network science, machine learning in Python, or statistical learning, there is a book on this list for you. So what are you waiting for? Start reading and start learning!

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