The Best Graph Databases Books for Beginners

Build machine learning algorithms using graph data and efficiently exploit topological information within your models. Key Features:. Implement machine learning techniques and algorithms in graph dataIdentify the relationship between nodes in order to make better business decisionsApply graph-based machine learning methods to solve real-life problems. Book Description:. Graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks.

The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You'll then learn all you need to know about 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 build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you'll be taken through 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. You'll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs. By the end of this 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. What You Will Learn:. Write Python scripts to extract features from graphsDistinguish between the main graph representation learning techniquesLearn how to extract data from social networks, financial transaction systems, for text analysis, and moreImplement the main unsupervised and supervised graph embedding techniquesGet to grips with shallow embedding methods, graph neural networks, graph regularization methods, and moreDeploy and scale out your application seamlessly. Who this book is for:. This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. It will also be useful for machine learning developers or anyone who wants to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required, alongside a solid understanding of ML basics. You'll also need intermediate-level Python programming knowledge to get started with this book.

Welcome to the world of graph database modeling! In today's interconnected and data-driven landscape, understanding the nuances of graph databases is paramount for anyone seeking to harness the power of relationships within their data. This book serves as your comprehensive guide to navigating the intricacies of graph database modeling, from conceptualizing complex networks to implementing efficient data structures.In these pages, you will embark on a journey that explores the fundamental principles of graph database design, offering practical insights and real-world examples to illuminate the path forward.

Whether you're a seasoned database professional looking to expand your repertoire or a curious beginner eager to delve into the world of graphs, this book provides the tools and knowledge you need to succeed. Drawing from years of experience and expertise, I have crafted this book to be a valuable resource for anyone interested in harnessing the power of graph databases. Each chapter is designed to build upon the last, guiding you through the process of modeling diverse datasets with clarity and precision. As you embark on this exploration, I encourage you to embrace curiosity, creativity, and a spirit of discovery. The world of graph database modeling is rich with possibility, and we are excited to be your companions on this journey. Happy modeling!

Unlock the power of connected data networks with "Graph Databases: Uncovering Insights in Connected Data Networks." This comprehensive guide dives deep into the essentials of graph databases, providing practical techniques and insights for analyzing and modeling data relationships. From understanding graph theory fundamentals to mastering graph algorithms, this book equips you with the knowledge and tools needed to harness the full potential of graph databases.

Explore real-world applications in social networks, recommendation systems, fraud detection, healthcare, and more, as you discover how graph databases revolutionize data analysis and decision-making processes. With step-by-step tutorials and case studies, you'll learn data modeling techniques, query optimization strategies, and performance benchmarking methods, enabling you to unlock actionable insights and drive innovation in your organization. Whether you're a beginner or an experienced data professional, "Graph Databases" provides invaluable insights and best practices for mastering this powerful technology. Dive into the world of connected data analysis today and transform the way you leverage data for strategic decision-making and business success.