國立虎尾科技大學 |

Computational advances in data analytics with social networks.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Computational advances in data analytics with social networks./
作者:	Khopkar, Sushant.
面頁冊數:	1 online resource (104 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.
Contained By:	Dissertation Abstracts International78-07B(E).
標題:	Industrial engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781369593778

Computational advances in data analytics with social networks.
Khopkar, Sushant.

Computational advances in data analytics with social networks. - 1 online resource (104 pages)

Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

The rise of computational power in the past decade has opened new opportunities for data analysis. At around the same time, an exponential growth in the Internet usage accelerated the generation of humongous amounts of data. The ability to quickly access these multi-faceted data and the availability of ever-increasing computational power led to a rapid development of the field of analytics.

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

Mode of access: World Wide Web

ISBN: 9781369593778Subjects--Topical Terms:

679492
Industrial engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Computational advances in data analytics with social networks.
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100 1 $a Khopkar, Sushant. $3 1180592
245 1 0 $a Computational advances in data analytics with social networks.
264 0 $c 2017
300 $a 1 online resource (104 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.
500 $a Advisers: Rakesh Nagi; Alexander G. Nikolaev.
502 $a Thesis (Ph.D.) $c State University of New York at Buffalo $d 2017.
504 $a Includes bibliographical references
520 $a The rise of computational power in the past decade has opened new opportunities for data analysis. At around the same time, an exponential growth in the Internet usage accelerated the generation of humongous amounts of data. The ability to quickly access these multi-faceted data and the availability of ever-increasing computational power led to a rapid development of the field of analytics.
520 $a Internet data are available in various formats; social networks is one type/form of these data. Prior to the availability of such data, in the early 20th century, sociologists used to interview people to understand their social connections and in this manner, used to form small social networks for analysis. Today, due to the activity on social networking platforms such as Facebook, Twitter, LinkedIn and Quora, it is possible to study social networks with millions of nodes and billions of edges.
520 $a This dissertation advances the research in social network analysis. It is divided into three chapters. The first two chapters present new developments in algorithm design and analysis, to enable efficient and fast computations with and inference from social network data. In the third chapter, a new network formation model is presented, with the aim to understand and explain laws behind the emergence of large social and other types of networks.
520 $a Chapter 1 presents incremental algorithms for all pairs shortest paths (APSP) and two centrality metrics in social network analysis viz., closeness and betweenness. These algorithms are useful when an extra node with edges or an extra edge is added to an existing social network. In this case, instead of re-computing everything from scratch, these algorithms intelligently and efficiently perform only the necessary update operations.
520 $a Chapter 2 of the dissertation presents approximation algorithms for two centrality metrics (closeness and betweenness) of social networks, along with their analysis and experimental comparison with other existing algorithms. Centrality metrics assign quantitative scores to each node denoting its importance, based on its network position. Sometimes, it is more important to rank order individuals quickly based on their importance, rather than accurately calculating the actual score values (which can be computationally expensive). The presented algorithms are useful exactly for such applications.
520 $a Chapter 3 develops a "popularity spread model" based on local visibility that spans "small-world" through "scale-free" networks. It attempts to explain why we observe power laws in the degree distributions of large networks. The model provides insights about how the exposure of an individual person/object to the masses can propagate through a word- of-mouth type principle. The logic of the model is thus based on stochastic percolation processes on small-world networks. A parameter estimation algorithm with the help of BOBYQA (Bound Optimization BY Quadratic Approximation) algorithm is used for esti- mating the model parameters for real-world social, citation and communication networks. It is a derivative-free numerical optimization algorithm.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Industrial engineering. $3 679492
650 4 $a Operations research. $3 573517
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0796
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a State University of New York at Buffalo. $b Industrial Engineering. $3 1180593
773 0 $t Dissertation Abstracts International $g 78-07B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10255672 $z click for full text (PQDT)