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001 9781003218869
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040 _aOCoLC-P
_beng
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_cOCoLC-P
020 _a9781003218869
_q(electronic bk.)
020 _a1003218865
_q(electronic bk.)
020 _a9781000515909
_q(electronic bk. : EPUB)
020 _a1000515907
_q(electronic bk. : EPUB)
020 _a9781000515855
_q(electronic bk. : PDF)
020 _a1000515850
_q(electronic bk. : PDF)
020 _z9781032112039
020 _z9781032112053
020 _z1032112034
035 _a(OCoLC)1273728308
035 _a(OCoLC-P)1273728308
050 4 _aQA76.9.D343
072 7 _aMAT
_x008000
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072 7 _aCOM
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072 7 _aCOM
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072 7 _aPBD
_2bicssc
082 0 4 _a006.312
_223
100 1 _aKamiński, Bogumił.
_971627
245 1 0 _aMining Complex Networks.
250 _aFirst edition.
264 1 _a[Place of publication not identified] :
_bChapman and Hall/CRC,
_c2021.
300 _a1 online resource (xiv, 264 pages).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
505 0 _aPreface I Core Material 1.Graph Theory2.Random Graph Models3.Centrality Measures4.Degree Correlations5.Community Detection6.Graph Embeddings7.HypergraphsII Complementary Material8.Detecting Overlapping Communities9.Embedding Graphs10.Network Robustness11.Road Networks
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.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aData mining.
_93907
650 0 _aOnline social networks
_xData processing.
_971628
650 7 _aMATHEMATICS / Discrete Mathematics
_2bisacsh
_913210
650 7 _aCOMPUTERS / Networking / General
_2bisacsh
_98506
650 7 _aCOMPUTERS / Machine Theory
_2bisacsh
_971629
700 1 _aPrałat, Paweł.
_971630
700 1 _aThéberge, François.
_971631
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9781003218869
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942 _cEBK
999 _c83078
_d83078