{"product_id":"network-models-for-data-science-theory-algorithms-and-applications-9781108835763","title":"Network Models for Data Science: Theory, Algorithms, and Applications","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003eThis text provides an introduction to the theory and applications of network science, covering topics such as probability models, graph theory, and computational algorithms. It demonstrates concepts in the context of real-world applications, including financial institutions, genes, neurons, and terrorist groups. Methods and models discussed include random graph models, percolation processes, network sampling, partitioning, and community detection. Dynamic networks, such as epidemics, are also introduced. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Hardback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 550 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 05 January 2023\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Cambridge University Press\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eThis comprehensive text on the theory and applications of network science is designed to cater to a diverse audience, including beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering who work with large and complex relational data sets. It offers an exhilarating array of analysis tools, encompassing probability models, graph theory, and computational algorithms, which expose students to novel ways of thinking about data that deviate from traditional statistical approaches. Concepts are illustrated through real-world applications, such as the relationships between financial institutions, genes or proteins, neurons in the brain, and terrorist groups. Detailed methods and models are described, including random graph models, percolation processes, techniques for sampling from vast networks, network partitioning, and community detection. Furthermore, the book introduces dynamic networks, such as epidemics, where time plays a crucial role. By providing a thorough exploration of network science, this text equips students with the skills and knowledge necessary to analyze and understand complex systems characterized by interconnected entities and their interactions. It serves as a valuable resource for scholars and practitioners seeking to leverage the power of networks in various fields.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 1154g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 165 x 260 x 30 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9781108835763\u003c\/p\u003e","brand":"Alan JulianIzenman","offers":[{"title":"Hardback","offer_id":44094885069050,"sku":"9781108835763","price":56.17,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/1673614245602_book.jpg?v=1673941755","url":"https:\/\/shulphink.com\/products\/network-models-for-data-science-theory-algorithms-and-applications-9781108835763","provider":"Shulph Ink","version":"1.0","type":"link"}