{"product_id":"graph-algorithms-for-data-science","title":"Graph Algorithms For Data Science","description":"\u003cdiv class=\"product-page-section\" style='color: rgb(51, 51, 51); text-transform: none; text-indent: 0px; letter-spacing: normal; font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px; font-style: normal; font-weight: 300; margin-bottom: 36px; word-spacing: 0px; display: block; white-space: normal; box-sizing: border-box; orphans: 2; widows: 2; font-variant-ligatures: normal; font-variant-caps: normal; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;'\u003e  \u003cdiv class=\"product-page-section\" style=\"padding-bottom: 0px; margin-bottom: 0px; display: block; box-sizing: border-box;\"\u003e   \u003cb style=\"font-weight: bold; box-sizing: border-box;\"\u003eGraphs are the natural way to understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment.\u003c\/b\u003e   \u003cbr style=\"box-sizing: border-box;\"\u003e   \u003cbr style=\"box-sizing: border-box;\"\u003eIn   \u003cspan\u003e \u003c\/span\u003e   \u003ci style=\"box-sizing: border-box;\"\u003eGraph Algorithms for Data Science\u003c\/i\u003e   \u003cspan\u003e \u003c\/span\u003eyou will learn:   \u003cbr style=\"box-sizing: border-box;\"\u003e   \u003cbr style=\"box-sizing: border-box;\"\u003e   \u003cul style=\"padding-left: 17.5px; margin-top: 0px; margin-bottom: 10.5px; box-sizing: border-box;\"\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eLabeled-property graph modeling\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eConstructing a graph from structured data such as CSV or SQL\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eNLP techniques to construct a graph from unstructured data\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eCypher query language syntax to manipulate data and extract insights\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eSocial network analysis algorithms like PageRank and community detection\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eHow to translate graph structure to a ML model input with node embedding models\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eUsing graph features in node classification and link prediction workflows\u003c\/li\u003e   \u003c\/ul\u003e   \u003cbr style=\"box-sizing: border-box;\"\u003e   \u003ci style=\"padding-bottom: 0px; margin-bottom: 0px; box-sizing: border-box;\"\u003eGraph Algorithms for Data Science\u003c\/i\u003e   \u003cspan\u003e \u003c\/span\u003eis 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. 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.  \u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='color: rgb(51, 51, 51); text-transform: none; text-indent: 0px; letter-spacing: normal; font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px; font-style: normal; font-weight: 300; margin-bottom: 36px; word-spacing: 0px; display: block; white-space: normal; box-sizing: border-box; orphans: 2; widows: 2; font-variant-ligatures: normal; font-variant-caps: normal; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;'\u003e  \u003ch2 style='margin: 0px 0px 10px; color: inherit; text-transform: lowercase; line-height: 1.1; font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 27px; font-weight: bold; box-sizing: border-box;'\u003eabout the technology\u003c\/h2\u003e  \u003ca name=\"about-the-technology\" class=\"anchor\" style=\"color: rgb(64, 127, 191); padding-bottom: 0px; text-decoration: none; margin-bottom: 0px; display: block; visibility: hidden; position: relative; box-sizing: border-box; background-color: transparent;\"\u003e\u003c\/a\u003eGraphs reveal the relationships in your data. Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole. This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations. \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='color: rgb(51, 51, 51); text-transform: none; text-indent: 0px; letter-spacing: normal; font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px; font-style: normal; font-weight: 300; margin-bottom: 36px; word-spacing: 0px; display: block; white-space: normal; box-sizing: border-box; orphans: 2; widows: 2; font-variant-ligatures: normal; font-variant-caps: normal; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;'\u003e  \u003ch2 style='margin: 0px 0px 10px; color: inherit; text-transform: lowercase; line-height: 1.1; font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 27px; font-weight: bold; box-sizing: border-box;'\u003eabout the book\u003c\/h2\u003e  \u003ca name=\"about-the-book\" class=\"anchor\" style=\"color: rgb(64, 127, 191); text-decoration: none; display: block; visibility: hidden; position: relative; box-sizing: border-box; background-color: transparent;\"\u003e\u003c\/a\u003e  \u003ci style=\"padding-bottom: 0px; margin-bottom: 0px; box-sizing: border-box;\"\u003eGraph Algorithms for Data Science\u003c\/i\u003e  \u003cspan\u003e \u003c\/span\u003eteaches you how to construct graphs from both structured and unstructured data. You''ll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. The book explores common and useful graph algorithms like PageRank and community detection\/clustering algorithms. Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network! Finally, you''ll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks. \u003c\/div\u003e","brand":"MediaPlace","offers":[{"title":"Default Title","offer_id":57305391956350,"sku":"NW9781617299469","price":40.34,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1379\/1261\/files\/9781617299469.jpg?v=1778527446","url":"https:\/\/mediaplace.com\/products\/graph-algorithms-for-data-science","provider":"MediaPlace","version":"1.0","type":"link"}