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Graph-Based Approach on Social Data ...

Wang, Guan.

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Graph-Based Approach on Social Data Mining.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Graph-Based Approach on Social Data Mining./
Author:	Wang, Guan.
Description:	167 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-05(E), Section: B.
Contained By:	Dissertation Abstracts International76-05B(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3668648
ISBN:	9781321438512

Graph-Based Approach on Social Data Mining.
Wang, Guan.

Graph-Based Approach on Social Data Mining. - 167 p.

Source: Dissertation Abstracts International, Volume: 76-05(E), Section: B.

Thesis (Ph.D.)--University of Illinois at Chicago, 2014.

This item must not be sold to any third party vendors.

Powered by big data infrastructures, social network platforms are gathering data on many aspects of our daily lives. The online social world is reflecting our physical world in an increasingly detailed way by collecting people's individual biographies and their various of relationships with other people. Although massive amount of social data has been gathered, an urgent challenge remain unsolved, which is to discover meaningful knowledge that can empower the social platforms to really understand their users from different perspectives.

ISBN: 9781321438512Subjects--Topical Terms:

523869
Computer science.

Graph-Based Approach on Social Data Mining.
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100 1 $a Wang, Guan. $3 3177458
245 1 0 $a Graph-Based Approach on Social Data Mining.
300 $a 167 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-05(E), Section: B.
500 $a Adviser: Philip S. Yu.
502 $a Thesis (Ph.D.)--University of Illinois at Chicago, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a Powered by big data infrastructures, social network platforms are gathering data on many aspects of our daily lives. The online social world is reflecting our physical world in an increasingly detailed way by collecting people's individual biographies and their various of relationships with other people. Although massive amount of social data has been gathered, an urgent challenge remain unsolved, which is to discover meaningful knowledge that can empower the social platforms to really understand their users from different perspectives.
520 $a Motivated by this trend, my research addresses the reasoning and mathematical modeling behind interesting phenomena on social networks. Proposing graph based data mining framework regarding to heterogeneous data sources is the major goal of my research. The algorithms, by design, utilize graph structure with heterogeneous link and node features to creatively represent social networks' basic structures and phenomena on top of them.
520 $a The graph based heterogeneous mining methodology is proved to be effective on a series of knowledge discovery topics, including network structure and macro social pattern mining such as magnet community detection (87), social influence propagation and social similarity mining (85), and spam detection (86). The future work is to consider dynamic relation on social data mining and how graph based approaches adapt from the new situations.
590 $a School code: 0799.
650 4 $a Computer science. $3 523869
690 $a 0984
710 2 $a University of Illinois at Chicago. $b Computer Science. $3 2094830
773 0 $t Dissertation Abstracts International $g 76-05B(E).
790 $a 0799
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3668648