國立虎尾科技大學 |

Towards Deep Representation Learning for Heterogeneous Networks.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Towards Deep Representation Learning for Heterogeneous Networks./
Author:	Iyer, Roshni Girija.
Description:	1 online resource (140 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798382787619

Towards Deep Representation Learning for Heterogeneous Networks.
Iyer, Roshni Girija.

Towards Deep Representation Learning for Heterogeneous Networks. - 1 online resource (140 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2024.

Includes bibliographical references

Deep learning models for graph structured data are popular recent developments in representation learning. These models are essential for numerous knowledge-driven applications including knowledge completion, and semantic applications like semantic search, question answering, and recommender systems. In this dissertation, we will focus on methods for modeling the rich information in heterogeneous networks, by delving into our proposed state-of-the-art graph neural network architecture, knowledge graph embedding model, question answering system, and social network embedding model. While prior works have merely scratched the surface to exploit the comprehensive data available from heterogeneous information in graphs, in this work, we will thoroughly examine systematic methods for mining and modeling different levels of heterogeneity to build the next generation of powerful deep learning models.

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

Mode of access: World Wide Web

ISBN: 9798382787619Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Graph machine learningIndex Terms--Genre/Form:

554714
Electronic books.

Towards Deep Representation Learning for Heterogeneous Networks.
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500 $a Advisor: Sun, Yizhou;Wang, Wei.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2024.
504 $a Includes bibliographical references
520 $a Deep learning models for graph structured data are popular recent developments in representation learning. These models are essential for numerous knowledge-driven applications including knowledge completion, and semantic applications like semantic search, question answering, and recommender systems. In this dissertation, we will focus on methods for modeling the rich information in heterogeneous networks, by delving into our proposed state-of-the-art graph neural network architecture, knowledge graph embedding model, question answering system, and social network embedding model. While prior works have merely scratched the surface to exploit the comprehensive data available from heterogeneous information in graphs, in this work, we will thoroughly examine systematic methods for mining and modeling different levels of heterogeneity to build the next generation of powerful deep learning models.
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538 $a Mode of access: World Wide Web
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653 $a Graph Neural Networks
653 $a Information retrieval
653 $a Network science
653 $a Question answering
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