國立虎尾科技大學 |

Cohesive Subgraph Search Over Large Heterogeneous Information Networks

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Cohesive Subgraph Search Over Large Heterogeneous Information Networks/ by Yixiang Fang, Kai Wang, Xuemin Lin, Wenjie Zhang.
作者:	Fang, Yixiang.
其他作者:	Wang, Kai.
面頁冊數:	XIX, 74 p. 20 illus., 5 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Information storage and retrieval systems. -
電子資源:	https://doi.org/10.1007/978-3-030-97568-5
ISBN:	9783030975685

Cohesive Subgraph Search Over Large Heterogeneous Information Networks
Fang, Yixiang.

Cohesive Subgraph Search Over Large Heterogeneous Information Networks[electronic resource] /by Yixiang Fang, Kai Wang, Xuemin Lin, Wenjie Zhang. - 1st ed. 2022. - XIX, 74 p. 20 illus., 5 illus. in color.online resource. - 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

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

561170
Information storage and retrieval systems.

LC Class. No.: QA75.5-76.95

Dewey Class. No.: 025.04

Cohesive Subgraph Search Over Large Heterogeneous Information Networks
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