大葉大學圖書館 |

Network data mining and analysis[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	006.3/12
書名/作者:	Network data mining and analysis/ Ming Gao, Ee-Peng Lim, David Lo.
作者:	Gao, Ming.
其他作者:	Lim, Ee-Peng.
出版者:	Singapore : : World Scientific,, c2019.
面頁冊數:	1 online resource (205 p.) : : ill. (some col.)
標題:	Data mining.
標題:	Electronic books.
ISBN:	9789813274969
書目註:	Includes bibliographical references (p. 169-176) and index.
摘要、提要註:	"Online social networking sites like Facebook, LinkedIn, and Twitter, offer millions of members the opportunity to befriend one another, send messages to each other, and post content on the site — actions which generate mind-boggling amounts of data every day. To make sense of the massive data from these sites, we resort to social media mining to answer questions like the following: What are social communities in bipartite graphs and signed graphs? How robust are the networks? How can we apply the robustness of networks? How can we find identical social users across heterogeneous social networks? Social media shatters the boundaries between the real world and the virtual world. We can now integrate social theories with computational methods to study how individuals interact with each other and how social communities form in bipartite and signed networks. The uniqueness of social media data calls for novel data mining techniques that can effectively handle user generated content with rich social relations. The study and development of these new techniques are under the purview of social media mining, an emerging discipline under the umbrella of data mining. Social Media Mining is the process of representing, analyzing, and extracting actionable patterns from social media data."--
電子資源:	https://www.worldscientific.com/worldscibooks/10.1142/11120#t=toc

Network data mining and analysis[electronic resource] /
Gao, Ming.

Network data mining and analysis[electronic resource] /Ming Gao, Ee-Peng Lim, David Lo. - 1st ed. - Singapore :World Scientific,c2019. - 1 online resource (205 p.) :ill. (some col.) - East China Normal University scientific reports,v. 82382-5715 ;. - East China Normal University scientific reports ;6..

Includes bibliographical references (p. 169-176) and index.

"Online social networking sites like Facebook, LinkedIn, and Twitter, offer millions of members the opportunity to befriend one another, send messages to each other, and post content on the site — actions which generate mind-boggling amounts of data every day. To make sense of the massive data from these sites, we resort to social media mining to answer questions like the following: What are social communities in bipartite graphs and signed graphs? How robust are the networks? How can we apply the robustness of networks? How can we find identical social users across heterogeneous social networks? Social media shatters the boundaries between the real world and the virtual world. We can now integrate social theories with computational methods to study how individuals interact with each other and how social communities form in bipartite and signed networks. The uniqueness of social media data calls for novel data mining techniques that can effectively handle user generated content with rich social relations. The study and development of these new techniques are under the purview of social media mining, an emerging discipline under the umbrella of data mining. Social Media Mining is the process of representing, analyzing, and extracting actionable patterns from social media data."--

Electronic reproduction.
Singapore :
World Scientific,
[2018]

Mode of access: World Wide Web.

ISBN: 9789813274969Subjects--Topical Terms:

337740
Data mining.

LC Class. No.: QA76.9.D343 / G36 2019

Dewey Class. No.: 006.3/12

Network data mining and analysis[electronic resource] /
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520 $a "Online social networking sites like Facebook, LinkedIn, and Twitter, offer millions of members the opportunity to befriend one another, send messages to each other, and post content on the site — actions which generate mind-boggling amounts of data every day. To make sense of the massive data from these sites, we resort to social media mining to answer questions like the following: What are social communities in bipartite graphs and signed graphs? How robust are the networks? How can we apply the robustness of networks? How can we find identical social users across heterogeneous social networks? Social media shatters the boundaries between the real world and the virtual world. We can now integrate social theories with computational methods to study how individuals interact with each other and how social communities form in bipartite and signed networks. The uniqueness of social media data calls for novel data mining techniques that can effectively handle user generated content with rich social relations. The study and development of these new techniques are under the purview of social media mining, an emerging discipline under the umbrella of data mining. Social Media Mining is the process of representing, analyzing, and extracting actionable patterns from social media data."-- $c Publisher's website.
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