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    Cohesive Subgraph Computation over Large Sparse Graphs: Algorithms, Data Structures, and Programming Techniques

    Cohesive Subgraph Computation over Large Sparse Graphs by Chang, Lijun; Qin, Lu;

    Algorithms, Data Structures, and Programming Techniques

    Series: Springer Series in the Data Sciences;

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      • Publisher's listprice EUR 53.49
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        22 690 Ft (21 609 Ft + 5% VAT)
      • Discount 8% (cc. 1 815 Ft off)
      • Discounted price 20 874 Ft (19 880 Ft + 5% VAT)

    22 690 Ft

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    Availability

    Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
    Not in stock at Prospero.

    Why don't you give exact delivery time?

    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Product details:

    • Edition number 1st ed. 2018
    • Publisher Springer
    • Date of Publication 7 January 2019
    • Number of Volumes 1 pieces, Book

    • ISBN 9783030035983
    • Binding Hardback
    • No. of pages107 pages
    • Size 235x155 mm
    • Weight 454 g
    • Language English
    • Illustrations 20 Illustrations, black & white; 1 Illustrations, color
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    Short description:

    This book is considered the first extended survey on algorithms and techniques for efficient cohesive subgraph computation. With rapid development of information technology, huge volumes of graph data are accumulated. An availability of rich graph data not only brings great opportunities for realizing big values of data to serve key applications, but also brings great challenges in computation. Using a consistent terminology, the book gives an excellent introduction to the models and algorithms for the problem of cohesive subgraph computation. The materials of this book are well organized from introductory content to more advanced topics while also providing well-designed source codes for most algorithms described in the book.
     
    This is a timely book for researchers who are interested in this topic and efficient data structure design for large sparse graph processing. It is also a guideline book for new researchers to get to know the area of cohesive subgraph computation.

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    Long description:

    This book is considered the first extended survey on algorithms and techniques for efficient cohesive subgraph computation. With rapid development of information technology, huge volumes of graph data are accumulated. An availability of rich graph data not only brings great opportunities for realizing big values of data to serve key applications, but also brings great challenges in computation. Using a consistent terminology, the book gives an excellent introduction to the models and algorithms for the problem of cohesive subgraph computation. The materials of this book are well organized from introductory content to more advanced topics while also providing well-designed source codes for most algorithms described in the book.
     
    This is a timely book for researchers who are interested in this topic and efficient data structure design for large sparse graph processing. It is also a guideline book for new researchers to get to know the area of cohesive subgraph computation.

    More

    Table of Contents:

    Introduction.- Linear Heap Data Structures.- Minimum Degree-based Core Decomposition.- Average Degree-based Densest Subgraph Computation.- Higher-order Structure-based Graph Decomposition.- Edge Connectivity-based Graph Decomposition.

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