國立虎尾科技大學 |

Graph Analysis on Social Networks.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Graph Analysis on Social Networks./
作者:	Lu, Shen.
面頁冊數:	1 online resource (217 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-10, Section: B.
Contained By:	Dissertations Abstracts International84-10B.
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798379402716

Graph Analysis on Social Networks.
Lu, Shen.

Graph Analysis on Social Networks. - 1 online resource (217 pages)

Source: Dissertations Abstracts International, Volume: 84-10, Section: B.

Thesis (Ph.D.)--University of South Florida, 2023.

Includes bibliographical references

With the development of transportation network, social network, and communication network, there are many applications in streaming data. For example, traffic congestion happens between the origin and destination of daily trips. Traffic analysis can help plan the trips so that traffic congestion can be avoided. Social network and communication network represent the behaviors of the entire population. People build connections based on their hobbies, daily activities, photos, videos, simple messages, and even anonymous web surfing. All of these can be turned into commercial use, such as product marketing, business network building, and technology trending. Data science is about how to model data for data issues, domain specific patterns, etc. If the sample set is big enough and the data is relevant, it is possible to engineer this process and to generate results. Once the data model is built, we can fit the model with the data and run proper algorithms to get answers. However, the challenges can be from data store, sample quality to information extraction.Especially for graph analysis, it needs to deal with not only the valuation of the vertices but also the connections between vertices, and how many connections each vertex can have. According to empirical experiments, the distributions of vertices, edges, and derived measurements, such as closeness, betweenness, and clustering coefficient have different distributions. When we work on data modeling strategies, both graph properties and topology types need to be under.In this dissertation, we discuss how to efficiently perform information extraction from graphs. Graphs can be considered as the structural representation of the social networks regarding the natural properties of information, such as regency, relevance, valuation, and validation. We focus on data loading, graph sampling, and application development.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379402716Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Attention mechanismIndex Terms--Genre/Form:

554714
Electronic books.

Graph Analysis on Social Networks.
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502 $a Thesis (Ph.D.)--University of South Florida, 2023.
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653 $a Graph convolution network
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