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Cohesive subgraph search over large heterogeneous information networks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Cohesive subgraph search over large heterogeneous information networksby Yixiang Fang ... [et al.].
other author:	Fang, Yixiang.
Published:	Cham :Springer International Publishing :2022.
Description:	xix, 74 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Data mining.
Online resource:	https://doi.org/10.1007/978-3-030-97568-5
ISBN:	9783030975685$q(electronic bk.)

Cohesive subgraph search over large heterogeneous information networks
Cohesive subgraph search over large heterogeneous information networks[electronic resource] /by Yixiang Fang ... [et al.]. - Cham :Springer International Publishing :2022. - xix, 74 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

Introduction -- Preliminaries -- CSS on Bipartite Networks -- CSS on Other General HINs -- Comparison Analysis -- Related Work on CSMs and solutions -- Future Work and Conclusion.

This SpringerBrief provides the first systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs) It also covers the research breakthroughs of this area, including models, algorithms and comparison studies in recent years. This SpringerBrief offers a list of promising future research directions of performing CSS over large HINs. The authors first classify the existing works of CSS over HINs according to the classic cohesiveness metrics such as core, truss, clique, connectivity, density, etc., and then extensively review the specific models and their corresponding search solutions in each group. Note that since the bipartite network is a special case of HINs, all the models developed for general HINs can be directly applied to bipartite networks, but the models customized for bipartite networks may not be easily extended for other general HINs due to their restricted settings. The authors also analyze and compare these cohesive subgraph models (CSMs) and solutions systematically. Specifically, the authors compare different groups of CSMs and analyze both their similarities and differences, from multiple perspectives such as cohesiveness constraints, shared properties, and computational efficiency. Then, for the CSMs in each group, the authors further analyze and compare their model properties and high-level algorithm ideas. This SpringerBrief targets researchers, professors, engineers and graduate students, who are working in the areas of graph data management and graph mining. Undergraduate students who are majoring in computer science, databases, data and knowledge engineering, and data science will also want to read this SpringerBrief.

ISBN: 9783030975685$q(electronic bk.)

Standard No.: 10.1007/978-3-030-97568-5doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Cohesive subgraph search over large heterogeneous information networks
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520 $a This SpringerBrief provides the first systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs) It also covers the research breakthroughs of this area, including models, algorithms and comparison studies in recent years. This SpringerBrief offers a list of promising future research directions of performing CSS over large HINs. The authors first classify the existing works of CSS over HINs according to the classic cohesiveness metrics such as core, truss, clique, connectivity, density, etc., and then extensively review the specific models and their corresponding search solutions in each group. Note that since the bipartite network is a special case of HINs, all the models developed for general HINs can be directly applied to bipartite networks, but the models customized for bipartite networks may not be easily extended for other general HINs due to their restricted settings. The authors also analyze and compare these cohesive subgraph models (CSMs) and solutions systematically. Specifically, the authors compare different groups of CSMs and analyze both their similarities and differences, from multiple perspectives such as cohesiveness constraints, shared properties, and computational efficiency. Then, for the CSMs in each group, the authors further analyze and compare their model properties and high-level algorithm ideas. This SpringerBrief targets researchers, professors, engineers and graduate students, who are working in the areas of graph data management and graph mining. Undergraduate students who are majoring in computer science, databases, data and knowledge engineering, and data science will also want to read this SpringerBrief.
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