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Probabilistic approaches for social ...

Yue, Kun.

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Probabilistic approaches for social media analysis = data, community and influence /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Probabilistic approaches for social media analysis/ Kun Yue ... [et al.].
其他題名:	data, community and influence /
其他作者:	Yue, Kun.
出版者:	Singapore :World Scientific, : c2020.,
面頁冊數:	1 online resource (292 p.)
內容註:	Introduction -- Adaptive and parallel acquisition of social media data from online big graphs -- A Bayesian network-based approach for incremental learning of uncertain knowledge -- Discovering user similarities in social behavioral interactions based on Bayesian network -- Associative categorization of frequent patterns in social media based on Markov network -- Markov network based latent link discovery and community detection in social behavioral interactions -- Probabilistic inferences of latent entity associations in textual web contents -- Containment of competitive influence spread on social networks -- Locating sources in online social networks via random walk.
標題:	Social media - Data processing. -
電子資源:	https://www.worldscientific.com/worldscibooks/10.1142/11476#t=toc
ISBN:	9789811207389

Probabilistic approaches for social media analysis = data, community and influence /
Probabilistic approaches for social media analysisdata, community and influence /[electronic resource] :Kun Yue ... [et al.]. - 1st ed. - Singapore :World Scientific,c2020. - 1 online resource (292 p.) - East China Normal University scientific reports,vol. 112382-5715 ;. - East China Normal University scientific reports ;vol. 11..

Includes bibliographical references and index.

Introduction -- Adaptive and parallel acquisition of social media data from online big graphs -- A Bayesian network-based approach for incremental learning of uncertain knowledge -- Discovering user similarities in social behavioral interactions based on Bayesian network -- Associative categorization of frequent patterns in social media based on Markov network -- Markov network based latent link discovery and community detection in social behavioral interactions -- Probabilistic inferences of latent entity associations in textual web contents -- Containment of competitive influence spread on social networks -- Locating sources in online social networks via random walk.

"This unique compendium focuses on the acquisition and analysis of social media data. The approaches concern both the data-intensive characteristics and graphical structures of social media. The book addresses the critical problems in social media analysis, which representatively cover its lifecycle. The must-have volume is an excellent reference text for professionals, researchers, academics and graduate students in AI and databases"--Publisher's website.

Mode of access: World Wide Web.

ISBN: 9789811207389Subjects--Topical Terms:

3512745
Social media
--Data processing.

LC Class. No.: HM742 / .P679 2020

Dewey Class. No.: 302.23/1

Probabilistic approaches for social media analysis = data, community and influence /
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504 $a Includes bibliographical references and index.
505 0 $a Introduction -- Adaptive and parallel acquisition of social media data from online big graphs -- A Bayesian network-based approach for incremental learning of uncertain knowledge -- Discovering user similarities in social behavioral interactions based on Bayesian network -- Associative categorization of frequent patterns in social media based on Markov network -- Markov network based latent link discovery and community detection in social behavioral interactions -- Probabilistic inferences of latent entity associations in textual web contents -- Containment of competitive influence spread on social networks -- Locating sources in online social networks via random walk.
520 $a "This unique compendium focuses on the acquisition and analysis of social media data. The approaches concern both the data-intensive characteristics and graphical structures of social media. The book addresses the critical problems in social media analysis, which representatively cover its lifecycle. The must-have volume is an excellent reference text for professionals, researchers, academics and graduate students in AI and databases"--Publisher's website.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat reader.
588 $a Description based on print version record.
650 0 $a Social media $x Data processing. $3 3512745
650 0 $a Text processing (Computer science) $3 532552
650 0 $a Quantitative research $x Statistical methods. $3 3512746
650 0 $a Machine learning. $3 533906
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