Authors:
- Includes data structures that can be of general use for efficient graph processing
- Considered the first extended survey on algorithms and techniques for efficient cohesive subgraph computation
- Source code of highly optimized algorithms is provided
Part of the book series: Springer Series in the Data Sciences (SSDS)
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Table of contents (6 chapters)
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Front Matter
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Back Matter
About this 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.
Authors and Affiliations
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School of Computer Science, The University of Sydney, Sydney, Australia
Lijun Chang
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Centre for Artificial Intelligence, University of Technology Sydney, Sydney, Australia
Lu Qin
Bibliographic Information
Book Title: Cohesive Subgraph Computation over Large Sparse Graphs
Book Subtitle: Algorithms, Data Structures, and Programming Techniques
Authors: Lijun Chang, Lu Qin
Series Title: Springer Series in the Data Sciences
DOI: https://doi.org/10.1007/978-3-030-03599-0
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2018
Hardcover ISBN: 978-3-030-03598-3Published: 07 January 2019
eBook ISBN: 978-3-030-03599-0Published: 24 December 2018
Series ISSN: 2365-5674
Series E-ISSN: 2365-5682
Edition Number: 1
Number of Pages: XII, 107
Number of Illustrations: 20 b/w illustrations, 1 illustrations in colour
Topics: Algorithms, Data Structures