國立虎尾科技大學 |

Diffusion in Social Networks

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Diffusion in Social Networks/ by Paulo Shakarian, Abhivav Bhatnagar, Ashkan Aleali, Elham Shaabani, Ruocheng Guo.
作者:	Shakarian, Paulo.
其他作者:	Bhatnagar, Abhivav.
面頁冊數:	XI, 101 p. 28 illus., 24 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-3-319-23105-1
ISBN:	9783319231051

Diffusion in Social Networks
Shakarian, Paulo.

Diffusion in Social Networks[electronic resource] /by Paulo Shakarian, Abhivav Bhatnagar, Ashkan Aleali, Elham Shaabani, Ruocheng Guo. - 1st ed. 2015. - XI, 101 p. 28 illus., 24 illus. in color.online resource. - SpringerBriefs in Computer Science,2191-5768. - SpringerBriefs in Computer Science,.

Introduction -- The SIR Model and Identification of Spreaders -- The Tipping Model and the Minimum Seed Problem -- The Independent Cascade and Linear Threshold Models -- Logic Programming Based Diffusion Models -- Evolutionary Graph Theory -- Examining Diffusion in the Real World -- Conclusion.

This book presents the leading models of social network diffusion that are used to demonstrate the spread of disease, ideas, and behavior. It introduces diffusion models from the fields of computer science (independent cascade and linear threshold), sociology (tipping models), physics (voter models), biology (evolutionary models), and epidemiology (SIR/SIS and related models). A variety of properties and problems related to these models are discussed including identifying seeds sets to initiate diffusion, game theoretic problems, predicting diffusion events, and more. The book explores numerous connections between social network diffusion research and artificial intelligence through topics such as agent-based modeling, logic programming, game theory, learning, and data mining. The book also surveys key empirical results in social network diffusion, and reviews the classic and cutting-edge research with a focus on open problems.

ISBN: 9783319231051

Standard No.: 10.1007/978-3-319-23105-1doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Diffusion in Social Networks
LDR:02630nam a22003975i 4500 001 963249
003 DE-He213
005 20200705080248.0
007 cr nn 008mamaa
008 201211s2015 gw | s |||| 0|eng d
020 $a 9783319231051 $9 978-3-319-23105-1
024 7 $a 10.1007/978-3-319-23105-1 $2 doi
035 $a 978-3-319-23105-1
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Shakarian, Paulo. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1027011
245 1 0 $a Diffusion in Social Networks $h [electronic resource] / $c by Paulo Shakarian, Abhivav Bhatnagar, Ashkan Aleali, Elham Shaabani, Ruocheng Guo.
250 $a 1st ed. 2015.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a XI, 101 p. 28 illus., 24 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a SpringerBriefs in Computer Science, $x 2191-5768
505 0 $a Introduction -- The SIR Model and Identification of Spreaders -- The Tipping Model and the Minimum Seed Problem -- The Independent Cascade and Linear Threshold Models -- Logic Programming Based Diffusion Models -- Evolutionary Graph Theory -- Examining Diffusion in the Real World -- Conclusion.
520 $a This book presents the leading models of social network diffusion that are used to demonstrate the spread of disease, ideas, and behavior. It introduces diffusion models from the fields of computer science (independent cascade and linear threshold), sociology (tipping models), physics (voter models), biology (evolutionary models), and epidemiology (SIR/SIS and related models). A variety of properties and problems related to these models are discussed including identifying seeds sets to initiate diffusion, game theoretic problems, predicting diffusion events, and more. The book explores numerous connections between social network diffusion research and artificial intelligence through topics such as agent-based modeling, logic programming, game theory, learning, and data mining. The book also surveys key empirical results in social network diffusion, and reviews the classic and cutting-edge research with a focus on open problems.
650 0 $a Artificial intelligence. $3 559380
650 0 $a Data encryption (Computer science). $3 1051084
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Cryptology. $3 1211076
700 1 $a Bhatnagar, Abhivav. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1258192
700 1 $a Aleali, Ashkan. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1258193
700 1 $a Shaabani, Elham. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1258194
700 1 $a Guo, Ruocheng. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1258195
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319231044
776 0 8 $i Printed edition: $z 9783319231068
830 0 $a SpringerBriefs in Computer Science, $x 2191-5768 $3 1255334
856 4 0 $u https://doi.org/10.1007/978-3-319-23105-1
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)