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 workflowsGraph 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 technologyA 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 bookGraph 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 insideCreating knowledge graphsNode classification and link prediction workflowsNLP techniques for graph constructionAbout the readerFor data scientists who know machine learning basics. Examples use the Cypher query language, which is explained in the book.About the AuthorTomaz Bratanic is a network scientist at heart, working at the intersection of graphs and machine learning. He has applied these graph techniques to projects in various domains including fraud detection, biomedicine, business-oriented analytics, and recommendations. Table of ContentsPART 1 INTRODUCTION TO GRAPHS1 Graphs and network science: An introduction2 Representing network structure: Designing your first graph modelPART 2 SOCIAL NETWORK ANALYSIS3 Your first steps with Cypher query language4 Exploratory graph analysis5 Introduction to social network analysis6 Projecting monopartite networks7 Inferring co-occurrence networks based on bipartite networks8 Constructing a nearest neighbor similarity networkPART 3 GRAPH MACHINE LEARNING9 Node embeddings and classification10 Link prediction11 Knowledge graph completion12 Constructing a graph using natural language processing technique