Isbn: 9783030975678 - cohesive subgraph search over large heterogeneous information networks (springerbriefs in computer science) (14 resultados)

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  • Idioma: Inglés

    Editorial: Springer, 2022

    3030975673 / 9783030975678

    Serie: Libro 9 de 60 - SpringerBriefs in Computer Science

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    Editorial: Springer, 2022

    3030975673 / 9783030975678

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    Condición: New. In English.

  • Idioma: Inglés

    Editorial: Springer, 2022

    3030975673 / 9783030975678

    Serie: Libro 9 de 60 - SpringerBriefs in Computer Science

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    Editorial: Springer, 2022

    3030975673 / 9783030975678

    Serie: Libro 9 de 60 - SpringerBriefs in Computer Science

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    Condición: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.

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    Editorial: Springer, 2022

    3030975673 / 9783030975678

    Serie: Libro 9 de 60 - SpringerBriefs in Computer Science

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  • Idioma: Inglés

    Editorial: Springer, 2022

    3030975673 / 9783030975678

    Serie: Libro 9 de 60 - SpringerBriefs in Computer Science

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  • Idioma: Inglés

    Editorial: Springer, 2022

    3030975673 / 9783030975678

    Serie: Libro 9 de 60 - SpringerBriefs in Computer Science

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    Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

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    Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

  • Idioma: Inglés

    Editorial: Springer, 2022

    3030975673 / 9783030975678

    Serie: Libro 9 de 60 - SpringerBriefs in Computer Science

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    Taschenbuch. Condición: Neu. Cohesive Subgraph Search Over Large Heterogeneous Information Networks | Yixiang Fang (u. a.) | Taschenbuch | SpringerBriefs in Computer Science | xix | Englisch | 2022 | Springer | EAN 9783030975678 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

  • Idioma: Inglés

    Editorial: Springer, 2022

    3030975673 / 9783030975678

    Serie: Libro 9 de 60 - SpringerBriefs in Computer Science

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    Condición: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.

  • Idioma: Inglés

    Editorial: Springer International Publishing Mai 2022, 2022

    3030975673 / 9783030975678

    Serie: Libro 9 de 60 - SpringerBriefs in Computer Science

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    Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 96 pp. Englisch.

  • Idioma: Inglés

    Editorial: Springer, 2022

    3030975673 / 9783030975678

    Serie: Libro 9 de 60 - SpringerBriefs in Computer Science

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    Editorial: Springer, 2022

    3030975673 / 9783030975678

    Serie: Libro 9 de 60 - SpringerBriefs in Computer Science

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  • Idioma: Inglés

    Editorial: Springer, Berlin|Springer International Publishing|Springer, 2022

    3030975673 / 9783030975678

    Serie: Libro 9 de 60 - SpringerBriefs in Computer Science

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    Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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.

  • Idioma: Inglés

    Editorial: Springer, Springer International Publishing Mai 2022, 2022

    3030975673 / 9783030975678

    Serie: Libro 9 de 60 - SpringerBriefs in Computer Science

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    Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 96 pp. Englisch.