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Graph Powered Analytics And Machine Learning With Tigergraph

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Graph Powered Analytics And Machine Learning With Tigergraph

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Author : Victor Lee Ph.D
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-07-24

Graph Powered Analytics And Machine Learning With Tigergraph written by Victor Lee Ph.D and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-24 with Computers categories.

With the rapid rise of graph databases, organizations are now implementing advanced analytics and machine learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database models available. You'll explore a three-stage approach to deriving value from connected data: connect, analyze, and learn. Victor Lee, Phuc Kien Nguyen, and Alexander Thomas present real use cases covering several contemporary business needs. By diving into hands-on exercises using TigerGraph Cloud, you'll quickly become proficient at designing and managing advanced analytics and machine learning solutions for your organization. Use graph thinking to connect, analyze, and learn from data for advanced analytics and machine learning Learn how graph analytics and machine learning can deliver key business insights and outcomes Use five core categories of graph algorithms to drive advanced analytics and machine learning Deliver a real-time 360-degree view of core business entities, including customer, product, service, supplier, and citizen Discover insights from connected data through machine learning and advanced analytics

Graph Powered Machine Learning

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Author : Alessandro Negro
language : en
Publisher: Simon and Schuster
Release Date : 2021-10-05

Graph Powered Machine Learning written by Alessandro Negro and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-05 with Computers categories.

Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. Summary In Graph-Powered Machine Learning, you will learn: The lifecycle of a machine learning project Graphs in big data platforms Data source modeling using graphs Graph-based natural language processing, recommendations, and fraud detection techniques Graph algorithms Working with Neo4J Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You’ll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro’s extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems. About the book Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you’ll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks. What's inside Graphs in big data platforms Recommendations, natural language processing, fraud detection Graph algorithms Working with the Neo4J graph database About the reader For readers comfortable with machine learning basics. About the author Alessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science. Table of Contents PART 1 INTRODUCTION 1 Machine learning and graphs: An introduction 2 Graph data engineering 3 Graphs in machine learning applications PART 2 RECOMMENDATIONS 4 Content-based recommendations 5 Collaborative filtering 6 Session-based recommendations 7 Context-aware and hybrid recommendations PART 3 FIGHTING FRAUD 8 Basic approaches to graph-powered fraud detection 9 Proximity-based algorithms 10 Social network analysis against fraud PART 4 TAMING TEXT WITH GRAPHS 11 Graph-based natural language processing 12 Knowledge graphs

Graph Powered Analytics And Machine Learning With Tigergraph

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Author : Victor Lee Ph.D
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-07-24

Graph Machine Learning

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Author : Claudio Stamile
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-06-25

Graph Machine Learning written by Claudio Stamile and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-25 with Computers categories.

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 data Identify the relationship between nodes in order to make better business decisions Apply 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 graphs Distinguish between the main graph representation learning techniques Learn how to extract data from social networks, financial transaction systems, for text analysis, 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 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.

Hands On Graph Analytics With Neo4j

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Author : Estelle Scifo
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-08-21

Hands On Graph Analytics With Neo4j written by Estelle Scifo and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-21 with Computers categories.

Discover how to use Neo4j to identify relationships within complex and large graph datasets using graph modeling, graph algorithms, and machine learning Key FeaturesGet up and running with graph analytics with the help of real-world examplesExplore various use cases such as fraud detection, graph-based search, and recommendation systemsGet to grips with the Graph Data Science library with the help of examples, and use Neo4j in the cloud for effective application scalingBook Description Neo4j is a graph database that includes plugins to run complex graph algorithms. The book starts with an introduction to the basics of graph analytics, the Cypher query language, and graph architecture components, and helps you to understand why enterprises have started to adopt graph analytics within their organizations. You’ll find out how to implement Neo4j algorithms and techniques and explore various graph analytics methods to reveal complex relationships in your data. You’ll be able to implement graph analytics catering to different domains such as fraud detection, graph-based search, recommendation systems, social networking, and data management. You’ll also learn how to store data in graph databases and extract valuable insights from it. As you become well-versed with the techniques, you’ll discover graph machine learning in order to address simple to complex challenges using Neo4j. You will also understand how to use graph data in a machine learning model in order to make predictions based on your data. Finally, you’ll get to grips with structuring a web application for production using Neo4j. By the end of this book, you’ll not only be able to harness the power of graphs to handle a broad range of problem areas, but you’ll also have learned how to use Neo4j efficiently to identify complex relationships in your data. What you will learnBecome well-versed with Neo4j graph database building blocks, nodes, and relationshipsDiscover how to create, update, and delete nodes and relationships using Cypher queryingUse graphs to improve web search and recommendationsUnderstand graph algorithms such as pathfinding, spatial search, centrality, and community detectionFind out different steps to integrate graphs in a normal machine learning pipelineFormulate a link prediction problem in the context of machine learningImplement graph embedding algorithms such as DeepWalk, and use them in Neo4j graphsWho this book is for This book is for data analysts, business analysts, graph analysts, and database developers looking to store and process graph data to reveal key data insights. This book will also appeal to data scientists who want to build intelligent graph applications catering to different domains. Some experience with Neo4j is required.

Graph Data Science With Python And Neo4j

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Author : Timothy Eastridge
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2024-03-11

Graph Data Science With Python And Neo4j written by Timothy Eastridge and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-11 with Computers categories.

Practical approaches to leveraging graph data science to solve real-world challenges. KEY FEATURES ● Explore the fundamentals of graph data science, its importance, and applications. ● Learn how to set up Python and Neo4j environments for graph data analysis. ● Discover techniques to visualize complex graph networks for better understanding. DESCRIPTION Graph Data Science with Python and Neo4j is your ultimate guide to unleashing the potential of graph data science by blending Python's robust capabilities with Neo4j's innovative graph database technology. From fundamental concepts to advanced analytics and machine learning techniques, you'll learn how to leverage interconnected data to drive actionable insights. Beyond theory, this book focuses on practical application, providing you with the hands-on skills needed to tackle real-world challenges. You'll explore cutting-edge integrations with Large Language Models (LLMs) like ChatGPT to build advanced recommendation systems. With intuitive frameworks and interconnected data strategies, you'll elevate your analytical prowess. This book offers a straightforward approach to mastering graph data science. With detailed explanations, real-world examples, and a dedicated GitHub repository filled with code examples, this book is an indispensable resource for anyone seeking to enhance their data practices with graph technology. Join us on this transformative journey across various industries, and unlock new, actionable insights from your data. WHAT WILL YOU LEARN ● Set up and utilize Python and Neo4j environments effectively for graph analysis. ● Import and manipulate data within the Neo4j graph database using Cypher Query Language. ● Visualize complex graph networks to gain insights into data relationships and patterns. ● Enhance data analysis by integrating ChatGPT for context-rich data enrichment. ● Explore advanced topics including Neo4j vector indexing and Retrieval-Augmented Generation (RAG). ● Develop recommendation engines leveraging graph embeddings for personalized suggestions. ● Build and deploy recommendation systems and fraud detection models using graph techniques. ● Gain insights into the future trends and advancements shaping the field of graph data science. WHO IS THIS BOOK FOR? This book caters to a diverse audience interested in leveraging the power of graph data science using Python and Neo4j. It includes Data Science Professionals, Software Engineers, Academic Researchers, Business Analysts, and Technology Hobbyists. This comprehensive book equips readers from various backgrounds to effectively utilize graph data science in their respective fields. TABLE OF CONTENTS 1. Introduction to Graph Data Science 2. Getting Started with Python and Neo4j 3. Import Data into the Neo4j Graph Database 4. Cypher Query Language 5. Visualizing Graph Networks 6. Enriching Neo4j Data with ChatGPT 7. Neo4j Vector Index and Retrieval-Augmented Generation (RAG) 8. Graph Algorithms in Neo4j 9. Recommendation Engines Using Embeddings 10. Fraud Detection CLOSING SUMMARY The Future of Graph Data Science Index

Graph Algorithms For Data Science

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Author : Tomaž Bratanic
language : en
Publisher: Simon and Schuster
Release Date : 2024-03-12

Graph Algorithms For Data Science written by Tomaž Bratanic and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-12 with Computers categories.

Practical methods for analyzing your data with graphs, revealing hidden connections and new insights. Graphs are the natural way to represent and understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with concrete advice on implementation and deployment. You don’t need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. In Graph Algorithms for Data Science you will learn: Labeled-property graph modeling Constructing a graph from structured data such as CSV or SQL NLP techniques to construct a graph from unstructured data Cypher query language syntax to manipulate data and extract insights Social network analysis algorithms like PageRank and community detection How to translate graph structure to a ML model input with node embedding models Using graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It’s filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You’ll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. Foreword by Michael Hunger. About the technology A graph, put simply, is a network of connected data. Graphs are an efficient way to identify and explore the significant relationships naturally occurring within a dataset. This book presents the most important algorithms for graph data science with examples from machine learning, business applications, natural language processing, and more. About the book Graph Algorithms for Data Science shows you how to construct and analyze graphs from structured and unstructured data. In it, you’ll learn to apply graph algorithms like PageRank, community detection/clustering, and knowledge graph models by putting each new algorithm to work in a hands-on data project. This cutting-edge book also demonstrates how you can create graphs that optimize input for AI models using node embedding. What's inside Creating knowledge graphs Node classification and link prediction workflows NLP techniques for graph construction About the reader For data scientists who know machine learning basics. Examples use the Cypher query language, which is explained in the book. About the author Tomaž Bratanic works at the intersection of graphs and machine learning. Arturo Geigel was the technical editor for this book. Table of Contents PART 1 INTRODUCTION TO GRAPHS 1 Graphs and network science: An introduction 2 Representing network structure: Designing your first graph model PART 2 SOCIAL NETWORK ANALYSIS 3 Your first steps with Cypher query language 4 Exploratory graph analysis 5 Introduction to social network analysis 6 Projecting monopartite networks 7 Inferring co-occurrence networks based on bipartite networks 8 Constructing a nearest neighbor similarity network PART 3 GRAPH MACHINE LEARNING 9 Node embeddings and classification 10 Link prediction 11 Knowledge graph completion 12 Constructing a graph using natural language processing technique

Graph Algorithms

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Author : Mark Needham
language : en
Publisher: O'Reilly Media
Release Date : 2019-05-16

Graph Algorithms written by Mark Needham and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-16 with Computers categories.

Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark

Data Analytics On Graphs

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Author : Ljubisa Stankovic
language : en
Publisher:
Release Date : 2020-12-22

Data Analytics On Graphs written by Ljubisa Stankovic and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-22 with Data mining categories.

Aimed at readers with a good grasp of the fundamentals of data analytics, this book sets out the fundamentals of graph theory and the emerging mathematical techniques for the analysis of a wide range of data acquired on graph environments. This book will be a useful friend and a helpful companion to all involved in data gathering and analysis.

Data Analytics

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Author : Mohiuddin Ahmed
language : en
Publisher: CRC Press
Release Date : 2018-09-21

Data Analytics written by Mohiuddin Ahmed and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-21 with Computers categories.

Large data sets arriving at every increasing speeds require a new set of efficient data analysis techniques. Data analytics are becoming an essential component for every organization and technologies such as health care, financial trading, Internet of Things, Smart Cities or Cyber Physical Systems. However, these diverse application domains give rise to new research challenges. In this context, the book provides a broad picture on the concepts, techniques, applications, and open research directions in this area. In addition, it serves as a single source of reference for acquiring the knowledge on emerging Big Data Analytics technologies.

Graph Powered Analytics And Machine Learning With Tigergraph

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