Téléchargez le livre : Cohesive Subgraph Search Over Large Heterogeneous Information Networks

Cohesive Subgraph Search Over Large Heterogeneous Information Networks

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Éditeur :

Springer


Collection :

SpringerBriefs in Computer Science

Paru le : 2022-05-06

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Description

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.
Pages
74 pages
Collection
SpringerBriefs in Computer Science
Parution
2022-05-06
Marque
Springer
EAN papier
9783030975678
EAN PDF
9783030975685
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
7
Taille du fichier
1966 Ko
Prix
47,46 €
EAN EPUB
9783030975685
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
7
Taille du fichier
2939 Ko
Prix
47,46 €

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