000			a
999			_c30691 _d30691
008			220322b xxu||||| |||| 00| 0 eng d
020			_a9781032112039
082			_a006.312 _bKAM
100			_aKaminski, Bogumit
245			_aMining complex networks
260			_bChapman and Hall/ CRC Press _c2022 _aBoca Raton :
300			_axiii, 263 p. ; _bill., _c24 cm
365			_b74.99 _cGBP _d105.90
504			_aIncludes index.
520			_aThis book concentrates on mining networks, a subfield within data science. Data science uses scientific and computational tools to extract valuable knowledge from large data sets. Once data is processed and cleaned, it is analyzed and presented to support decision making processes. Data science and machine learning tools have become widely used in companies of all sizes. Networks are often large-scale, decentralized, and evolve dynamically over time. Mining complex networks to understand the principles governing the organization and the behaviour of such networks is crucial for a broad range of fields of study. Here are a few selected typical applications of mining networks: Community detection (which users on some social media platform are close friends), Link prediction (who is likely to connect to whom on such platforms), Node attribute prediction (what advertisement should be shown to a given user of a particular platform to match their interests), Influential node detection (which social media users would be the best ambassadors of a specific product). This textbook is suitable for an upper-year undergraduate course or a graduate course in programs such as data science, mathematics, computer science, business, engineering, physics, statistics, and social science. This book can be successfully used by all enthusiasts of data science at various levels of sophistication to expand their knowledge or consider changing their career path. Jupiter notebooks (in Python and Julia) accompany the book and can be accessed on https://www.ryerson.ca/mining-complex-networks/. These not only contain all of the experiments presented in the book yet also include additional material.
650			_aData mining
650			_aOnline social networks
650			_aData processing
650			_aAssortativity
650			_aBenchmark
650			_aClustering
650			_aEigenvalue
650			_aGraph G
650			_aHypergraph
650			_a Modularity
650			_aPartition
650			_aProbability
650			_aRandom graph
650			_aData Science
700			_aPrałat, Paweł
700			_aTheberge, Francois
942			_2ddc _cBK