LEADER 05086nim a22004817i 4500001 17144085 005 20240429162347.0 006 m o h 007 sz zunnnnnuneu 007 cr nnannnuuuuu 008 240401s2024 xx nnnn o z n eng d 035 (OCoLC)on1428328159 040 ORMDA |beng |erda |epn |cORMDA |dOCLCO 024 8 9781617299469AU 037 9781617299469AU |bO'Reilly Media 050 4 Q325.5 100 1 Bratanic, Tomaz, |eauthor. 245 10 Graph algorithms for data science : |bwith examples in Neo4j / |cTomaž Bratanič ; foreword by Michael Hunger. 250 [First edition]. 264 1 [Place of publication not identified] : |bManning Publications, |c2024. 300 1 online resource (1 sound file (9 hr., 35 min.)) 306 093500 336 spoken word |bspw |2rdacontent 337 computer |bc |2rdamedia 338 online resource |bcr |2rdacarrier 344 digital |2rdatr 347 audio file |2rdaft 506 Access restricted by licensing agreement. 520 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. 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 Toma¿ℓ Bratani♯⁻ works at the intersection of graphs and machine learning. Arturo Geigel was the technical editor for this book. Quotes Undoubtedly the quickest route to grasping the practical applications of graph algorithms. Enjoyable and informative, with real-world business context and practical problem-solving. - Roger Yu, Feedzai Brilliantly eases you into graph-based applications. - Sumit Pal, Independent Consultant I highly recommend this book to anyone involved in analyzing large network databases. - Ivan Herreros, talentsconnect Insightful and comprehensive. The author's expertise is evident. Be prepared for a rewarding journey. - Michal ¿ tefa¿⁸©Łk, Volke. 588 Online resource; title from title details screen (O'Reilly, viewed April 1, 2024). 590 Access is available to the Yale community. 650 0 Data mining. |0http://id.loc.gov/authorities/subjects/sh97002073 655 7 Audiobooks. |2lcgft |0http://id.loc.gov/authorities/genreForms/gf2011026063 700 1 Hunger, Michael, |ewriter of foreword. 730 0 O'Reilly Safari. |gOCLC KB. 852 80 |byulint |hNone |zOnline resource 852 80 |zOnline resource 856 40 |yStreaming audio |uhttps://go.oreilly.com/yale-university/library/view/-/9781617299469AU/?ar 901 Q325.5 902 Yale Internet Resource |bYale Internet Resource >> None|DELIM|17020847 905 online resource 907 2024-04-29T16:23:47.000Z 946 DO NOT EDIT. DO NOT EXPORT.