{"product_id":"9781617299469","title":"Graph Algorithms for Data Science: With examples in Neo4j","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eBratanic, Tomaž\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e2\/27\/2024\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003ePractical methods for analyzing your data with graphs, revealing hidden connections and new insights.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eGraphs 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.\u003cbr\u003e\u003cbr\u003eIn \u003ci\u003eGraph Algorithms for Data Science\u003c\/i\u003e you will learn:\u003cul\u003e \u003cli\u003eLabeled-property graph modeling\u003c\/li\u003e \u003cli\u003eConstructing a graph from structured data such as CSV or SQL\u003c\/li\u003e \u003cli\u003eNLP techniques to construct a graph from unstructured data\u003c\/li\u003e \u003cli\u003eCypher query language syntax to manipulate data and extract insights\u003c\/li\u003e \u003cli\u003eSocial network analysis algorithms like PageRank and community detection\u003c\/li\u003e \u003cli\u003eHow to translate graph structure to a ML model input with node embedding models\u003c\/li\u003e \u003cli\u003eUsing graph features in node classification and link prediction workflows\u003c\/li\u003e \u003c\/ul\u003e\u003cbr\u003e\u003ci\u003eGraph Algorithms for Data Science\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003eForeword by Michael Hunger.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eA 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003ci\u003eGraph Algorithms for Data Science\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eWhat's inside\u003c\/b\u003e\u003cul\u003e \u003cli\u003eCreating knowledge graphs\u003c\/li\u003e \u003cli\u003eNode classification and link prediction workflows\u003c\/li\u003e \u003cli\u003eNLP techniques for graph construction\u003c\/li\u003e \u003c\/ul\u003e\u003cbr\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eFor data scientists who know machine learning basics. Examples use the Cypher query language, which is explained in the book.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTomaž Bratanic\u003c\/b\u003e works at the intersection of graphs and machine learning.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eArturo Geigel\u003c\/b\u003e was the technical editor for this book.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003ePART 1 INTRODUCTION TO GRAPHS\u003cbr\u003e1 Graphs and network science: An introduction\u003cbr\u003e2 Representing network structure: Designing your first graph model\u003cbr\u003ePART 2 SOCIAL NETWORK ANALYSIS\u003cbr\u003e3 Your first steps with Cypher query language\u003cbr\u003e4 Exploratory graph analysis\u003cbr\u003e5 Introduction to social network analysis\u003cbr\u003e6 Projecting monopartite networks\u003cbr\u003e7 Inferring co-occurrence networks based on bipartite networks\u003cbr\u003e8 Constructing a nearest neighbor similarity network\u003cbr\u003ePART 3 GRAPH MACHINE LEARNING\u003cbr\u003e9 Node embeddings and classification\u003cbr\u003e10 Link prediction\u003cbr\u003e11 Knowledge graph completion\u003cbr\u003e12 Constructing a graph using natural language processing technique","brand":"Manning","offers":[{"title":"Default Title","offer_id":42973612441855,"sku":"9781617299469","price":59.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/Jacket_d41c9d44-f9fe-4a5f-a275-e5cbac903139.jpg?v=1771253659","url":"https:\/\/massivebookshop.com\/products\/9781617299469","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}