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The Cost of Sharing Information in a...

ProQuest Information and Learning Co.

The Cost of Sharing Information in a Social World.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	The Cost of Sharing Information in a Social World./
作者:	Ramachandran, Arthi.
面頁冊數:	1 online resource (135 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355170900

The Cost of Sharing Information in a Social World.
Ramachandran, Arthi.

The Cost of Sharing Information in a Social World. - 1 online resource (135 pages)

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

With the increasing prevalence of large scale online social networks, the field has evolved from studying small scale networks and interactions to massive ones that encompass huge fractions of the world's population. While many methods focus on techniques at scale applied to a single domain, methods that apply techniques across multiple domains are becoming increasingly important. These methods rely on understanding the complex relationships in the data. In the context of social networks, the big data available allows us to better model and analyze the flow of information within the network.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355170900Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

The Cost of Sharing Information in a Social World.
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245 1 4 $a The Cost of Sharing Information in a Social World.
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300 $a 1 online resource (135 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
500 $a Adviser: Augustin Chaintreau.
502 $a Thesis (Ph.D.) $c Columbia University $d 2017.
504 $a Includes bibliographical references
520 $a With the increasing prevalence of large scale online social networks, the field has evolved from studying small scale networks and interactions to massive ones that encompass huge fractions of the world's population. While many methods focus on techniques at scale applied to a single domain, methods that apply techniques across multiple domains are becoming increasingly important. These methods rely on understanding the complex relationships in the data. In the context of social networks, the big data available allows us to better model and analyze the flow of information within the network.
520 $a The first part of this thesis discusses methods to more effectively learn and predict in a social network by leveraging information across multiple domains and types of data. We document a method to identify users from their access to content in a network and their click behavior. Even on a macro level, click behavior is often hard to obtain. We describe a technique to predict click behavior using other public information about the social network.
520 $a Communication within a network inevitably has some bias that can be attributed to individual preferences and quality as well as the underlying structure of the network. The second part of the thesis characterizes the structural bias in a network by modeling the underlying information flow as a commodity of trade.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Web studies. $3 1148502
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0646
710 2 $a ProQuest Information and Learning Co. $3 1178819
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773 0 $t Dissertation Abstracts International $g 79-01B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10617535 $z click for full text (PQDT)

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