Cohesive subgraph search over large heterogeneous information networks / Yixiang Fang, Kai Wang, Xuemin Lin, Wenjie Zhang.
Fang, Yixiang, author.; Wang, Kai, author.; Lin, Xuemin, author.; Zhang, Wenjie (Computer scientist), author.
2022
QA76.9.D343
Available Online

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Title Cohesive subgraph search over large heterogeneous information networks / Yixiang Fang, Kai Wang, Xuemin Lin, Wenjie Zhang.
Author Fang, Yixiang, author.
ISBN 9783030975685 (electronic bk.)
3030975681 (electronic bk.)
9783030975678 (print)
3030975673
DOI https://doi.org/10.1007/978-3-030-97568-5
Published Cham, Switzerland : Springer, 2022.
Language English
Description 1 online resource (xix, 74 pages) : illustrations (some color).
Item Number 10.1007/978-3-030-97568-5 doi
Call Number QA76.9.D343
Dewey Decimal Classification 006.3/12
Summary 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed May 10, 2022).
Added Author Wang, Kai, author.
Lin, Xuemin, author.
Zhang, Wenjie (Computer scientist), author.
Series SpringerBriefs in computer science, 2191-5776
Available in Other Form Print version: 9783030975678
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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Introduction
Preliminaries
CSS on Bipartite Networks
CSS on Other General HINs
Comparison Analysis
Related Work on CSMs and solutions
Future Work and Conclusion.

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