Fang Yixiang, Wang Kai, Lin Xuemin Cohesive Subgraph Search Over Large Heterogeneous Information Networks 9783030975678

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Автор: Fang Yixiang, Wang Kai, Lin Xuemin
Название: Cohesive Subgraph Search Over Large Heterogeneous Information Networks
ISBN: 9783030975678
Издательство: Springer
Классификация:

Дискретная математика

Комбинаторика и теория графов

Хранилище данных

Информационный поиск

Математика для специалистов в области теории вычислительных систем

ISBN-10: 3030975673
Обложка/Формат: Paperback
Страницы: 96
Вес: 0.15 кг.
Дата издания: 07.05.2022
Серия: Springerbriefs in computer science
Язык: English
Издание: 1st ed. 2022
Иллюстрации: 5 tables, color; 5 illustrations, color; 15 illustrations, black and white; xix, 74 p. 20 illus., 5 illus. in color.
Размер: 234 x 156 x 5
Читательская аудитория: Professional & vocational
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: 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.
Дополнительное описание: Introduction.- Preliminaries.- CSS on Bipartite Networks.- CSS on Other General HINs.- Comparison Analysis.- Related Work on CSMs and solutions.- Future Work and Conclusion.

Cohesive Subgraph Search Over Large Heterogeneous Information Networks, Fang Yixiang, Wang Kai, Lin Xuemin