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Study of complex networks using stat...

Boston University.

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Study of complex networks using statistical physics methods.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Study of complex networks using statistical physics methods./
Author:	Chen, Yiping.
Description:	81 p.
Notes:	Adviser: H. Eugene Stanley.
Contained By:	Dissertation Abstracts International70-01B.
Subject:	Physics, Theory. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3345708
ISBN:	9781109003062

Study of complex networks using statistical physics methods.
Chen, Yiping.

Study of complex networks using statistical physics methods. - 81 p.

Adviser: H. Eugene Stanley.

Thesis (Ph.D.)--Boston University, 2009.

The goal of this thesis is to study the behaviors of complex networks in several aspects using methods from statistical physics. Networks are structures that consist of nodes and links. By changing the way links connect to nodes, different complex network structures can be constructed such as Erd&huml;s-Renyi (ER) networks and scale-free (SF) networks. Complex networks have wide relevance to many real world problems, including the spread of disease in human society, message routing in the Internet, etc. In this thesis analytical and simulation results are obtained regarding optimal paths in disordered networks, fragmentation of social networks, and improved strategies for immunization against diseases.

ISBN: 9781109003062Subjects--Topical Terms:

1019422
Physics, Theory.

Study of complex networks using statistical physics methods.
LDR:03153nam 2200289 a 45 001 855708
005 20100708
008 100708s2009 ||||||||||||||||| ||eng d
020 $a 9781109003062
035 $a (UMI)AAI3345708
035 $a AAI3345708
040 $a UMI $c UMI
100 1 $a Chen, Yiping. $3 1022367
245 1 0 $a Study of complex networks using statistical physics methods.
300 $a 81 p.
500 $a Adviser: H. Eugene Stanley.
500 $a Source: Dissertation Abstracts International, Volume: 70-01, Section: B, page: 0374.
502 $a Thesis (Ph.D.)--Boston University, 2009.
520 $a The goal of this thesis is to study the behaviors of complex networks in several aspects using methods from statistical physics. Networks are structures that consist of nodes and links. By changing the way links connect to nodes, different complex network structures can be constructed such as Erd&huml;s-Renyi (ER) networks and scale-free (SF) networks. Complex networks have wide relevance to many real world problems, including the spread of disease in human society, message routing in the Internet, etc. In this thesis analytical and simulation results are obtained regarding optimal paths in disordered networks, fragmentation of social networks, and improved strategies for immunization against diseases.
520 $a In the study of disordered systems, of particular current interest is the scaling behavior of the optimal path length &ell;opt from strong disorder to weak disorder state for different weight distributions P(w). Here we derive analytically a new criterion. Using this criterion we find that for all P(w) that possess a strong-weak disorder crossover, the distributions p(&ell;) of the optimal path lengths display the same universal behavior.
520 $a Fragmentation in social networks is also studied using methods from percolation theory. Recently, a new measure of fragmentation F has been developed in social network studies. For each removal of a subset of links or nodes, F is defined as the ratio between the number of pairs of nodes that are not connected in the fragmented network after removal, and the total number of pairs in the original fully connected network. We study the statistical behavior of F using both analytical and numerical methods and relate it to the traditional measure of fragmentation, the relative size of the largest cluster, Pinfinity, used in percolation theory. Finally, we tried to find a better immunization strategy. It is widely accepted that the most efficient immunization strategies are based on "targeted" strategies. Here we propose a novel "equal graph partitioning" immunization strategy which we find to be significantly better than targeted methods, with 5% to 50% fewer immunization doses required. We confirm the improved efficiency of our approach on ER and SF networks, random regular graphs, and on several real networks.
590 $a School code: 0017.
650 4 $a Physics, Theory. $3 1019422
690 $a 0753
710 2 $a Boston University. $3 1017454
773 0 $t Dissertation Abstracts International $g 70-01B.
790 $a 0017
790 1 0 $a Stanley, H. Eugene, $e advisor
791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3345708