東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Unsupervised Classification and Network Analysis of the Reddit Communities with Spiking Neural Network and Exponential-Family Random Graph Model.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Unsupervised Classification and Network Analysis of the Reddit Communities with Spiking Neural Network and Exponential-Family Random Graph Model./
作者:	He, Jie.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	54 p.
附註:	Source: Masters Abstracts International, Volume: 83-01.
Contained By:	Masters Abstracts International83-01.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28544601
ISBN:	9798516086656

Unsupervised Classification and Network Analysis of the Reddit Communities with Spiking Neural Network and Exponential-Family Random Graph Model.
He, Jie.

Unsupervised Classification and Network Analysis of the Reddit Communities with Spiking Neural Network and Exponential-Family Random Graph Model. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 54 p.

Source: Masters Abstracts International, Volume: 83-01.

Thesis (M.S.)--University of California, Los Angeles, 2021.

This item must not be sold to any third party vendors.

The spiking neural networks (SNNs) are often described as the "third generation" of neural networks, and they are expected to improve the existing deep neural networks. Recent advancements of SNNs mainly focused on processing and learning the visual signals, while SNNs' potential in classifying non-image data is rarely tested. In this thesis, we extended the functionality of BindsNET, a popular SNN simulation software, to allow it to process and classify non-image data. We built an SNN that can efficiently classify the embedding data of 51,278 online communities ("subreddits") on Reddit.com in an unsupervised fashion. With the classification result, we further analyzed the social network structure of the subreddit clusters of video games, using the exponential-family random graph model (ERGM). We discovered that communities of the same video game genre or same platform are more likely to be hostile towards each other. The number of subscribers and the availability of online mode are also significant factors in the hostility of a subreddit.

ISBN: 9798516086656Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Network analysis

Unsupervised Classification and Network Analysis of the Reddit Communities with Spiking Neural Network and Exponential-Family Random Graph Model.
LDR:02230nmm a2200361 4500 001 2349561
005 20230509091113.5
006 m o d
007 cr#unu||||||||
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798516086656
035 $a (MiAaPQ)AAI28544601
035 $a AAI28544601
040 $a MiAaPQ $c MiAaPQ
100 1 $a He, Jie. $3 3549736
245 1 0 $a Unsupervised Classification and Network Analysis of the Reddit Communities with Spiking Neural Network and Exponential-Family Random Graph Model.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 54 p.
500 $a Source: Masters Abstracts International, Volume: 83-01.
500 $a Advisor: Wu, Yingnian.
502 $a Thesis (M.S.)--University of California, Los Angeles, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a The spiking neural networks (SNNs) are often described as the "third generation" of neural networks, and they are expected to improve the existing deep neural networks. Recent advancements of SNNs mainly focused on processing and learning the visual signals, while SNNs' potential in classifying non-image data is rarely tested. In this thesis, we extended the functionality of BindsNET, a popular SNN simulation software, to allow it to process and classify non-image data. We built an SNN that can efficiently classify the embedding data of 51,278 online communities ("subreddits") on Reddit.com in an unsupervised fashion. With the classification result, we further analyzed the social network structure of the subreddit clusters of video games, using the exponential-family random graph model (ERGM). We discovered that communities of the same video game genre or same platform are more likely to be hostile towards each other. The number of subscribers and the availability of online mode are also significant factors in the hostility of a subreddit.
590 $a School code: 0031.
650 4 $a Statistics. $3 517247
650 4 $a Information technology. $3 532993
653 $a Network analysis
653 $a Reddit
653 $a Neural network
690 $a 0463
690 $a 0489
710 2 $a University of California, Los Angeles. $b Statistics 0891. $3 2095317
773 0 $t Masters Abstracts International $g 83-01.
790 $a 0031
791 $a M.S.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28544601