Graph Machine Learning – Take Graph Data to the Next Level by Applying Machine Learning Techniques and Algorithms

Graph Machine Learning - Take Graph Data to the Next Level by Applying Machine Learning Techniques and Algorithms

Graph Machine Learning provides a new set of tools for processing network data and leveraging the power of the relationship 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, learning to understand 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 on, you will cover real-world scenarios, such as extracting data from social networks, text analytics, and natural language processing 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.

By the end of this machine learning book, you will have learned the essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications.

Who this book is for

This book is for data analysts, graph developers, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points, unravel hidden structures, and exploit topological information to boost their analysis and models’ performance. The book will also be useful for data scientists and machine learning developers who want to build machine learning-driven graph databases. A beginner-level understanding of graph databases and graph data is required. An intermediate-level working knowledge of Python programming and machine learning is also expected to make the most out of this book.

What this book covers

Chapter 1, Getting Started with Graphs, introduces the basic concepts of graph theory using the NetworkX Python library.

Chapter 2, Graph Machine Learning, introduces the main concepts of graph machine learning and graph embedding techniques.

Chapter 3, Unsupervised Graph Learning, covers recent unsupervised graph embedding methods.

Chapter 4, Supervised Graph Learning, covers recent supervised graph embedding methods.

Chapter 5, Problems with Machine Learning on Graphs, introduces the most common machine learning tasks on graphs.

Chapter 6, Social Network Analysis, shows an application of machine learning algorithms on social network data.

Chapter 7, Text Analytics and Natural Language Processing Using Graphs, shows the application of machine learning algorithms to natural language processing tasks.

Chapter 8, Graph Analysis for Credit Card Transactions, shows the application of machine learning algorithms to credit card fraud detection.

Chapter 9, Building a Data-Driven Graph-Powered Application, introduces some technologies and techniques that are useful for dealing with large graphs.

Chapter 10, Novel Trends on Graphs, introduces some novel trends (algorithms and applications) in graph machine learning.


  • 关于本书的内容介绍、目录、详情等请在 AmazonGoolge Books 等售书网站搜索查看,本站仅展示封面作为参考。
  • 如无特殊说明,本站提供的所有pdf均为文字版(aka True PDF or Digitally Created PDF)。
  • 本站已经列出的所有图书均可以找到。
  • 收到PDF链接之后建议尽快下载或者保存到自己的百度网盘,防止链接过期失效。


扫描下方二维码添加微信号 bookyage 回复本书编号 227618 即可,我们会尽快(一般24小时之内)将本书PDF文件以百度网盘链接的形式发送给您。