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Cohesive Subgraph Search Over Large Heterogeneous Information NetworksCSS on Other General HINs

Cohesive Subgraph Search Over Large Heterogeneous Information Networks: CSS on Other General HINs [In the era of big data, most of data or informational objects, individual agents, groups, or components are interconnected or interact with each other, forming numerous, large, interconnected, and sophisticated networks, which are often called heterogeneous information networks (HINs) in the literature. For instance, Twitter contains 326 million monthly active users in over 160 countries, and they generate over 500 million daily tweets, including texts, images, videos, events, etc.—all these information naturally forms a huge HIN. Consequently, the sheer volume and the complex structure of HIN data become bottlenecks in querying cohesive subgraphs from large scale HINs. With strong demands from a number of recent real applications, the field of CSS over HINs has drawn a great deal of attention in the last two decades and a number of research breakthroughs have been reported, including novel CSMs and solutions. In this chapter, we extensively review the CSMs and solutions over general HINs that are not customized for bipartite networks, and may have various vertex types and edge types. We first introduce some key concepts on the general HINs, and then thoroughly review five groups of CSMs and solutions that are classified according to the cohesiveness metric.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Cohesive Subgraph Search Over Large Heterogeneous Information NetworksCSS on Other General HINs

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Publisher
Springer International Publishing
Copyright
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
ISBN
978-3-030-97567-8
Pages
27 –46
DOI
10.1007/978-3-030-97568-5_4
Publisher site
See Chapter on Publisher Site

Abstract

[In the era of big data, most of data or informational objects, individual agents, groups, or components are interconnected or interact with each other, forming numerous, large, interconnected, and sophisticated networks, which are often called heterogeneous information networks (HINs) in the literature. For instance, Twitter contains 326 million monthly active users in over 160 countries, and they generate over 500 million daily tweets, including texts, images, videos, events, etc.—all these information naturally forms a huge HIN. Consequently, the sheer volume and the complex structure of HIN data become bottlenecks in querying cohesive subgraphs from large scale HINs. With strong demands from a number of recent real applications, the field of CSS over HINs has drawn a great deal of attention in the last two decades and a number of research breakthroughs have been reported, including novel CSMs and solutions. In this chapter, we extensively review the CSMs and solutions over general HINs that are not customized for bipartite networks, and may have various vertex types and edge types. We first introduce some key concepts on the general HINs, and then thoroughly review five groups of CSMs and solutions that are classified according to the cohesiveness metric.]

Published: Feb 23, 2022

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