Graph Machine Learning
- Paperback: 338 pages
- Publisher: WOW! eBook (June 25, 2021)
- Language: English
- ISBN-10: 1800204493
- ISBN-13: 978-1800204492
Graph Machine Learning: Build machine learning algorithms using graph data and efficiently exploit topological information within your models
Graph-Powered Machine Learning provides a new set of tools for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks.
You will start with a brief introduction to graph theory and graph machine learning, understanding their potential. As you proceed, you will become well versed with the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You’ll then build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. Moving ahead, you will cover real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. Finally, you will learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, before progressing to explore the latest trends on graphs.
- Write Python scripts to extract features from graphs
- Distinguish between the main graph representation learning techniques
- Become well-versed with extracting data from social networks, financial transaction systems, and more
- Implement the main unsupervised and supervised graph embedding techniques
- Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more
- Deploy and scale out your application seamlessly
By the end of this Graph Machine Learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications.
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