國立虎尾科技大學 |

Topics in Graph Clustering.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Topics in Graph Clustering./
作者:	Wan, Yali.
面頁冊數:	1 online resource (118 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:	Dissertation Abstracts International79-04B(E).
標題:	Statistics. -
電子資源:	click for full text (PQDT)
ISBN:	9780355356588

Topics in Graph Clustering.
Wan, Yali.

Topics in Graph Clustering. - 1 online resource (118 pages)

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

In this thesis, two problems in social networks will be studied.

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

Mode of access: World Wide Web

ISBN: 9780355356588Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Topics in Graph Clustering.
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100 1 $a Wan, Yali. $3 1183013
245 1 0 $a Topics in Graph Clustering.
264 0 $c 2017
300 $a 1 online resource (118 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
500 $a Adviser: Marina Meila.
502 $a Thesis (Ph.D.) $c University of Washington $d 2017.
504 $a Includes bibliographical references
520 $a In this thesis, two problems in social networks will be studied.
520 $a In the first part of the thesis, we focus on community recovery problems for social networks. There have been many recent theoretical advances in the model-based community recovery for network data. In the center of it are the Stochastic Block Model (SBM) and its extension, Degree Corrected Stochastic Block Model (DC-SBM). Under assumptions on the balance and separation of clusters, theoretical guarantees have been provided to ensure the recovery of the true clusters with high probability.We firstly benchmark the current recovery theorems on DC-SBM through experimental approaches. The experiments suggest that there are still lots of cases that are recoverable but not predicted by the current recovery theorems. We then introduce a wider class of network models called Preference Frame Model. We show that under weaker assumptions, the communities or clusters can be recovered by spectral clustering algorithm with essentially the same guarantees. The model-based results, despite their importance, are limited by a strong and difficult-to-verify assumption that the observed data are generated from the model. We present the model-free community recovery, where we do not make assumptions about the data generating process and provide theoretical guarantees for the performance of the model based clustering algorithms in this framework.
520 $a In the second part of the thesis, we propose a perturbation framework to measure the robustness of graph properties. Although there are already perturbation methods proposed to tackle this problem, they are limited by the fact that the strength of the perturbation cannot be well controlled. We firstly provide a perturbation framework on graphs by introducing weights on the nodes, of which the magnitude of perturbation can be easily controlled through the variance of the weights. Meanwhile, the topology of the graphs are also preserved to avoid uncontrollable strength in the perturbation. We then extend the measure of robustness in the robust statistics literature to the graph properties.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
650 4 $a Mathematics. $3 527692
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Washington. $b Statistics. $3 1182938
773 0 $t Dissertation Abstracts International $g 79-04B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10622616 $z click for full text (PQDT)