國立高雄大學圖資館 |

Language: English

Back

Heterogeneous graph representation learning and applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Heterogeneous graph representation learning and applicationsby Chuan Shi, Xiao Wang, Philip S. Yu.
Author:	Shi, Chuan.
other author:	Wang, Xiao.
Published:	Singapore :Springer Singapore :2022.
Description:	xx, 318 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Heterogeneous computing.
Online resource:	https://doi.org/10.1007/978-981-16-6166-2
ISBN:	9789811661662$q(electronic bk.)

Heterogeneous graph representation learning and applications
Shi, Chuan.

Heterogeneous graph representation learning and applications[electronic resource] /by Chuan Shi, Xiao Wang, Philip S. Yu. - Singapore :Springer Singapore :2022. - xx, 318 p. :ill., digital ;24 cm. - Artificial intelligence: foundations, theory, and algorithms,2365-306X. - Artificial intelligence: foundations, theory, and algorithms..

Introduction -- The State-of-the-art of Heterogeneous Graph Representation -- Part One: Techniques -- Structure-preserved Heterogeneous Graph Representation -- Attribute-assisted Heterogeneous Graph Representation -- Dynamic Heterogeneous Graph Representation -- Supplementary of Heterogeneous Graph Representation -- Part Two: Applications -- Heterogeneous Graph Representation for Recommendation -- Heterogeneous Graph Representation for Text Mining -- Heterogeneous Graph Representation for Industry Application -- Future Research Directions -- Conclusion.

Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node. In this book, we provide a comprehensive survey of current developments in HG representation learning. More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.

ISBN: 9789811661662$q(electronic bk.)

Standard No.: 10.1007/978-981-16-6166-2doiSubjects--Topical Terms:

224824
Heterogeneous computing.

LC Class. No.: QA76.88 / .S45 2022

Dewey Class. No.: 004.35

Heterogeneous graph representation learning and applications
LDR:03522nmm a2200361 a 4500 001 619011
003 DE-He213
005 20220130180009.0
006 m d
007 cr nn 008maaau
008 221103s2022 si s 0 eng d
020 $a 9789811661662$q(electronic bk.)
020 $a 9789811661655$q(paper)
024 7 $a 10.1007/978-981-16-6166-2 $2 doi
035 $a 978-981-16-6166-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.88 $b .S45 2022
072 7 $a UNF $2 bicssc
072 7 $a UYQE $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 004.35 $2 23
090 $a QA76.88 $b .S555 2022
100 1 $a Shi, Chuan. $3 785232
245 1 0 $a Heterogeneous graph representation learning and applications $h [electronic resource] / $c by Chuan Shi, Xiao Wang, Philip S. Yu.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2022.
300 $a xx, 318 p. : $b ill., digital ; $c 24 cm.
490 1 $a Artificial intelligence: foundations, theory, and algorithms, $x 2365-306X
505 0 $a Introduction -- The State-of-the-art of Heterogeneous Graph Representation -- Part One: Techniques -- Structure-preserved Heterogeneous Graph Representation -- Attribute-assisted Heterogeneous Graph Representation -- Dynamic Heterogeneous Graph Representation -- Supplementary of Heterogeneous Graph Representation -- Part Two: Applications -- Heterogeneous Graph Representation for Recommendation -- Heterogeneous Graph Representation for Text Mining -- Heterogeneous Graph Representation for Industry Application -- Future Research Directions -- Conclusion.
520 $a Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node. In this book, we provide a comprehensive survey of current developments in HG representation learning. More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.
650 0 $a Heterogeneous computing. $3 224824
650 0 $a Machine learning. $3 188639
650 1 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Data Science. $3 913495
700 1 $a Wang, Xiao. $3 676006
700 1 $a Yu, Philip S. $3 283991
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Artificial intelligence: foundations, theory, and algorithms. $3 729041
856 4 0 $u https://doi.org/10.1007/978-981-16-6166-2
950 $a Computer Science (SpringerNature-11645)