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    Home » Computers & Internet » Graph Algorithms for Data Science: With examples in Neo4j
    Computers & Internet

    Graph Algorithms for Data Science: With examples in Neo4j

    TeamBy TeamSeptember 29, 2024No Comments3 Mins Read0 Views
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    1727628914 71USmkf6PEL. SL1500 Graph Algorithms for Data Science: With examples in Neo4j Edu Expertise Hub data science
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    Price: $59.99 - $49.56
    (as of Sep 29, 2024 16:55:16 UTC – Details)

    amazon buy now button 300x148 1 Graph Algorithms for Data Science: With examples in Neo4j Edu Expertise Hub data science

    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 modelingConstructing a graph from structured data such as CSV or SQLNLP techniques to construct a graph from unstructured dataCypher query language syntax to manipulate data and extract insightsSocial network analysis algorithms like PageRank and community detectionHow to translate graph structure to a ML model input with node embedding modelsUsing 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.

    Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

    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 graphsNode classification and link prediction workflowsNLP 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

    From the Publisher

    Graph Algorithms for Data Science BannerGraph Algorithms for Data Science Banner

    Graph Algorithms for Data Science Quote1Graph Algorithms for Data Science Quote1

    Graph Algorithms for Data Science Quote2Graph Algorithms for Data Science Quote2

    Graph Algorithms for Data Science Quote3Graph Algorithms for Data Science Quote3

    Graph Algorithms for Data Science IncludesGraph Algorithms for Data Science Includes

    Publisher ‏ : ‎ Manning (February 27, 2024)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 352 pages
    ISBN-10 ‏ : ‎ 1617299464
    ISBN-13 ‏ : ‎ 978-1617299469
    Item Weight ‏ : ‎ 1.35 pounds
    Dimensions ‏ : ‎ 7.38 x 0.6 x 9.25 inches

    This post is exclusively published on eduexpertisehub.com
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