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Optimization in social networks.

The University of Texas at Dallas.

Optimization in social networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimization in social networks.
Author:	Zhu, Yuqing.
Description:	114 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.
Notes:	Adviser: Weili Wu.
Contained By:	Dissertation Abstracts International75-11B(E).
Subject:	Computer science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3629052
ISBN:	9781321057072

Optimization in social networks.
Zhu, Yuqing.

Optimization in social networks. - 114 p.

Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.

Thesis (Ph.D.)--The University of Texas at Dallas, 2014.

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

Social networks have shown increasing popularity in real-world applications. In this dissertation, I study several optimization problems in social networks. In Chapter 1, I propose an approximation algorithm for influence maximization problem in social networks which works better than the start-of-arts under certain circumstances. In Chapter 2, noticing that for a company, the prot and influence are often different, I propose the balanced influence and profit (BIP) problem and design effective algorithms. In Chapter 3, I propose a new influence diffusion model - Timeliness Independent Cascade (TIC) for the case where multiple companies spread their influence and compete each other in a social network. I present the FairInf problem aiming at giving different companies fair influence spreads under TIC model. Several algorithms are designed for FairInf problem. In Chapter 4, a new partitioning method for social networks has been devised. This method is based on the mutual relationship between each pair of individuals in the social network, and works better than existing partitioning strategy on real world datasets.

ISBN: 9781321057072Subjects--Topical Terms:

199325
Computer science.

Optimization in social networks.
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500 $a Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.
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520 $a Social networks have shown increasing popularity in real-world applications. In this dissertation, I study several optimization problems in social networks. In Chapter 1, I propose an approximation algorithm for influence maximization problem in social networks which works better than the start-of-arts under certain circumstances. In Chapter 2, noticing that for a company, the prot and influence are often different, I propose the balanced influence and profit (BIP) problem and design effective algorithms. In Chapter 3, I propose a new influence diffusion model - Timeliness Independent Cascade (TIC) for the case where multiple companies spread their influence and compete each other in a social network. I present the FairInf problem aiming at giving different companies fair influence spreads under TIC model. Several algorithms are designed for FairInf problem. In Chapter 4, a new partitioning method for social networks has been devised. This method is based on the mutual relationship between each pair of individuals in the social network, and works better than existing partitioning strategy on real world datasets.
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