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Deep Representation Learning on Comp...

Zheng, Cheng.

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Deep Representation Learning on Complex Graphs.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep Representation Learning on Complex Graphs./
作者:	Zheng, Cheng.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	142 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-04, Section: B.
Contained By:	Dissertations Abstracts International82-04B.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28093296
ISBN:	9798678122940

Deep Representation Learning on Complex Graphs.
Zheng, Cheng.

Deep Representation Learning on Complex Graphs. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 142 p.

Source: Dissertations Abstracts International, Volume: 82-04, Section: B.

Thesis (Ph.D.Eng.)--University of California, Los Angeles, 2020.

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

Graph representation learning serves as the core of many important tasks on graphs, ranging from friendship recommendation, name disambiguation, drug discovery, and fraud detection. Recently, deep learning has revolutionized various domains such as computer vision, natural language processing, speech recognition, etc. Inspired by the success of deep neural networks, there has been an increasing interest to learn graph representations with deep learning models such as autoencoders, convolutional neural networks, etc. However, graphs in real-life applications usually have complex structures such as sparse connections, task-irrelevant information, and rapidly evolving structures. The complexity poses great challenges to the existing frameworks, such as network embedding models with random walks and graph neural networks based on the neighborhood aggregation. In this dissertation, we propose several deep learning frameworks to tackle the aforementioned problems of graph representation learning on complex graphs. We propose a novel model to learn network representations with adversarially regularized autoencoders to overcome the sparse sampling issue of random walks on graphs. To resolve the task-irrelevant noise, we propose a general framework that is trained to simultaneously select task-relevant edges and learn graph representations by the feedback signals from downstream tasks. To learn from the dynamic evolving graphs, we propose to extract local features by performing convolutions in nodes' neighborhoods defined in joint temporal-structural space. The methodologies presented in these frameworks span different research areas, including deep network embedding, graph representation learning, temporal graph modeling, and node classification on graphs. As a result, these methodologies not only tackle specific challenges in the graph learning tasks mentioned above but also shed light on other applications like social network analysis.

ISBN: 9798678122940Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Graph representation

Deep Representation Learning on Complex Graphs.
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520 $a Graph representation learning serves as the core of many important tasks on graphs, ranging from friendship recommendation, name disambiguation, drug discovery, and fraud detection. Recently, deep learning has revolutionized various domains such as computer vision, natural language processing, speech recognition, etc. Inspired by the success of deep neural networks, there has been an increasing interest to learn graph representations with deep learning models such as autoencoders, convolutional neural networks, etc. However, graphs in real-life applications usually have complex structures such as sparse connections, task-irrelevant information, and rapidly evolving structures. The complexity poses great challenges to the existing frameworks, such as network embedding models with random walks and graph neural networks based on the neighborhood aggregation. In this dissertation, we propose several deep learning frameworks to tackle the aforementioned problems of graph representation learning on complex graphs. We propose a novel model to learn network representations with adversarially regularized autoencoders to overcome the sparse sampling issue of random walks on graphs. To resolve the task-irrelevant noise, we propose a general framework that is trained to simultaneously select task-relevant edges and learn graph representations by the feedback signals from downstream tasks. To learn from the dynamic evolving graphs, we propose to extract local features by performing convolutions in nodes' neighborhoods defined in joint temporal-structural space. The methodologies presented in these frameworks span different research areas, including deep network embedding, graph representation learning, temporal graph modeling, and node classification on graphs. As a result, these methodologies not only tackle specific challenges in the graph learning tasks mentioned above but also shed light on other applications like social network analysis.
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