東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Statistical Mechanics of the Communi...

Hu, Dandan.

Linked to FindBook

Google Book

Amazon

博客來

Statistical Mechanics of the Community Detection Problem: Theory and Application.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Statistical Mechanics of the Community Detection Problem: Theory and Application./
Author:	Hu, Dandan.
Description:	284 p.
Notes:	Source: Dissertation Abstracts International, Volume: 73-11(E), Section: B.
Contained By:	Dissertation Abstracts International73-11B(E).
Subject:	Physics, General. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3516724
ISBN:	9781267463609

Statistical Mechanics of the Community Detection Problem: Theory and Application.
Hu, Dandan.

Statistical Mechanics of the Community Detection Problem: Theory and Application. - 284 p.

Source: Dissertation Abstracts International, Volume: 73-11(E), Section: B.

Thesis (Ph.D.)--Washington University in St. Louis, 2012.

We study phase transitions in spin glass type systems and in related computational problems. In the current work, we focus on the "community detection" problem when cast in terms of a general Potts spin glass type problem. We report on phase transitions between solvable and unsolvable regimes. Solvable region may further split into easy and hard phases. Spin glass type phase transitions appear at both low and high temperatures. Low temperature transitions correspond to an order by disorder type effect wherein fluctuations render the system ordered or solvable. Separate transitions appear at higher temperatures into a disordered (or an unsolvable) phases. Different sorts of randomness lead to disparate behaviors. We illustrate the spin glass character of both transitions and report on memory effects. We further relate Potts type spin systems to mechanical analogs and suggest how chaotic-type behavior in general thermodynamic systems can indeed naturally arise in hard-computational problems and spin-glasses. In this work, we also examine large networks (with a power law distribution in cluster size) that have a large number of communities. We infer that large systems at a constant ratio of q to the number of nodes N asymptotically tend toward insolvability in the limit of large N for any positive temperature. We further employ multivariate Tutte polynomials to show that increasing q emulates increasing T for a general Potts model, leading to a similar stability region at low T.

ISBN: 9781267463609Subjects--Topical Terms:

1018488
Physics, General.

Statistical Mechanics of the Community Detection Problem: Theory and Application.
LDR:03459nam a2200301 4500 001 1958202
005 20140327081049.5
008 150210s2012 ||||||||||||||||| ||eng d
020 $a 9781267463609
035 $a (MiAaPQ)AAI3516724
035 $a AAI3516724
040 $a MiAaPQ $c MiAaPQ
100 1 $a Hu, Dandan. $3 2093217
245 1 0 $a Statistical Mechanics of the Community Detection Problem: Theory and Application.
300 $a 284 p.
500 $a Source: Dissertation Abstracts International, Volume: 73-11(E), Section: B.
500 $a Adviser: Zohar Nussinov.
502 $a Thesis (Ph.D.)--Washington University in St. Louis, 2012.
520 $a We study phase transitions in spin glass type systems and in related computational problems. In the current work, we focus on the "community detection" problem when cast in terms of a general Potts spin glass type problem. We report on phase transitions between solvable and unsolvable regimes. Solvable region may further split into easy and hard phases. Spin glass type phase transitions appear at both low and high temperatures. Low temperature transitions correspond to an order by disorder type effect wherein fluctuations render the system ordered or solvable. Separate transitions appear at higher temperatures into a disordered (or an unsolvable) phases. Different sorts of randomness lead to disparate behaviors. We illustrate the spin glass character of both transitions and report on memory effects. We further relate Potts type spin systems to mechanical analogs and suggest how chaotic-type behavior in general thermodynamic systems can indeed naturally arise in hard-computational problems and spin-glasses. In this work, we also examine large networks (with a power law distribution in cluster size) that have a large number of communities. We infer that large systems at a constant ratio of q to the number of nodes N asymptotically tend toward insolvability in the limit of large N for any positive temperature. We further employ multivariate Tutte polynomials to show that increasing q emulates increasing T for a general Potts model, leading to a similar stability region at low T.
520 $a We further apply the replica inference based Potts model method to unsupervised image segmentation on multiple scales. This approach was inspired by the statistical mechanics problem of "community detection" and its phase diagram. The problem is cast as identifying tightly bound clusters against a background. Within our multiresolution approach, we compute information theory based correlations among multiple solutions of the same graph over a range of resolutions. Significant multiresolution structures are identified by replica correlations as manifest in information overlaps. With the aid of these correlations as well as thermodynamic measures, the phase diagram of the corresponding Potts model is analyzed both at zero and finite temperatures. Optimal parameters corresponding to a sensible unsupervised segmentation correspond to the easy phase of the Potts model. Our algorithm is fast and shown to be at least as accurate as the best algorithms to date and to be especially suited to the detection of camouflage images.
590 $a School code: 0252.
650 4 $a Physics, General. $3 1018488
650 4 $a Physics, Theory. $3 1019422
650 4 $a Statistics. $3 517247
690 $a 0605
690 $a 0753
690 $a 0463
710 2 $a Washington University in St. Louis. $b Physics. $3 2093218
773 0 $t Dissertation Abstracts International $g 73-11B(E).
790 $a 0252
791 $a Ph.D.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3516724