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

切換: 標籤 | MARC模式 | ISBD

New Deep Neural Networks for Unsuper...

Gao, Hongchang.

FindBook

Google Book

Amazon

博客來

New Deep Neural Networks for Unsupervised Feature Learning on Graph Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	New Deep Neural Networks for Unsupervised Feature Learning on Graph Data./
作者:	Gao, Hongchang.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	121 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-09, Section: B.
Contained By:	Dissertations Abstracts International82-09B.
標題:	World Wide Web. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28370836
ISBN:	9798582553946

New Deep Neural Networks for Unsupervised Feature Learning on Graph Data.
Gao, Hongchang.

New Deep Neural Networks for Unsupervised Feature Learning on Graph Data. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 121 p.

Source: Dissertations Abstracts International, Volume: 82-09, Section: B.

Thesis (Ph.D.)--University of Pittsburgh, 2020.

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

Graph data are ubiquitous in the real world, such as social networks, biological networks. To analyze graph data, a fundamental task is to learn node features to benefit downstream tasks, such as node classification, community detection. Inspired by the powerful feature learning capability of deep neural networks on various tasks, it is important and necessary to explore deep neural networks for feature learning on graphs. Different from the regular image and sequence data, graph data encode the complicated relational information between different nodes, which challenges the classical deep neural networks. Moreover, in real-world applications, the label of nodes in graph data is usually not available, which makes the feature learning on graphs more difficult. To address these challenging issues, this thesis is focusing on designing new deep neural networks to effectively explore the relational information for unsupervised feature learning on graph data.First, to address the sparseness issue of the relational information, I propose a new proximity generative adversarial network which can discover the underlying relational information for learning better node representations. Meanwhile, a new self-paced network embedding method is designed to address the unbalance issue of the relational information when learning node representations. Additionally, to deal with rich attributes associated to nodes, I develop a new deep neural network to capture various relational information in both topological structure and node attributes for enhancing network embedding. Furthermore, to preserve the relational information in the hidden layers of deep neural networks, I develop a novel graph convolutional neural network (GCN) based on conditional random fields, which is the first algorithm applying this kind of graphical models to graph neural networks in an unsupervised manner.

ISBN: 9798582553946Subjects--Topical Terms:

534287
World Wide Web.
Subjects--Index Terms:

Information processing

New Deep Neural Networks for Unsupervised Feature Learning on Graph Data.
LDR:02968nmm a2200313 4500 001 2282929
005 20211022115802.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9798582553946
035 $a (MiAaPQ)AAI28370836
035 $a (MiAaPQ)Pittsburgh39284
035 $a AAI28370836
040 $a MiAaPQ $c MiAaPQ
100 1 $a Gao, Hongchang. $3 3561806
245 1 0 $a New Deep Neural Networks for Unsupervised Feature Learning on Graph Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 121 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-09, Section: B.
500 $a Advisor: Wei, Chen;Liang, Zhan;Wei, Gao;Zhi-Hong, Mao;Huang, Heng.
502 $a Thesis (Ph.D.)--University of Pittsburgh, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Graph data are ubiquitous in the real world, such as social networks, biological networks. To analyze graph data, a fundamental task is to learn node features to benefit downstream tasks, such as node classification, community detection. Inspired by the powerful feature learning capability of deep neural networks on various tasks, it is important and necessary to explore deep neural networks for feature learning on graphs. Different from the regular image and sequence data, graph data encode the complicated relational information between different nodes, which challenges the classical deep neural networks. Moreover, in real-world applications, the label of nodes in graph data is usually not available, which makes the feature learning on graphs more difficult. To address these challenging issues, this thesis is focusing on designing new deep neural networks to effectively explore the relational information for unsupervised feature learning on graph data.First, to address the sparseness issue of the relational information, I propose a new proximity generative adversarial network which can discover the underlying relational information for learning better node representations. Meanwhile, a new self-paced network embedding method is designed to address the unbalance issue of the relational information when learning node representations. Additionally, to deal with rich attributes associated to nodes, I develop a new deep neural network to capture various relational information in both topological structure and node attributes for enhancing network embedding. Furthermore, to preserve the relational information in the hidden layers of deep neural networks, I develop a novel graph convolutional neural network (GCN) based on conditional random fields, which is the first algorithm applying this kind of graphical models to graph neural networks in an unsupervised manner.
590 $a School code: 0178.
650 4 $a World Wide Web. $3 534287
650 4 $a Deep learning. $3 3554982
650 4 $a Artificial intelligence. $3 516317
650 4 $a Knowledge discovery. $3 3561807
650 4 $a Graph representations. $3 3560730
650 4 $a Neural networks. $3 677449
650 4 $a Teleconferencing. $3 898948
650 4 $a Information processing. $3 3561808
650 4 $a Distance learning. $3 3557921
650 4 $a Information retrieval. $3 566853
653 $a Information processing
690 $a 0800
710 2 $a University of Pittsburgh. $3 958527
773 0 $t Dissertations Abstracts International $g 82-09B.
790 $a 0178
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28370836