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New Deep Neural Networks for Unsuper...

Gao, Hongchang.

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	New Deep Neural Networks for Unsupervised Feature Learning on Graph Data./
Author:	Gao, Hongchang.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	121 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-09, Section: B.
Contained By:	Dissertations Abstracts International82-09B.
Subject:	World Wide Web. -
Online resource:	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.
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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.
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650 4 $a Distance learning. $3 3557921
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