Springer Series: Texts in Computer Science
Erciyes, K.
2018, 471 p. 247 illus.
·
ISBN 978-3319732343
Description fom the
Editor
This clearly
structured textbook/reference presents a detailed and comprehensive
review of the
fundamental principles of sequential graph algorithms, approaches for NP-hard graph
problems, and
approximation algorithms and heuristics for such problems.
The work also
provides a comparative
analysis of sequential, parallel and distributed
graph algorithms –
including algorithms
for big data
– and an investigation into the conversion
principles between
the three
algorithmic methods.Topics
and features: presents a comprehensive analysis of
sequential graph algorithms; offers a unifying view by examining
the same graph problem
from each
of the three paradigms of sequential, parallel and distributed
algorithms; describes
methods for
the conversion between sequential, parallel and distributed
graph algorithms;
surveys methods
for the analysis
of large graphs and complex network applications; includes
full implementation
details for the problems presented
throughout the text; provides additional
supporting material at an accompanying website. This practical
guide to the design and
analysis of graph
algorithms is ideal for advanced and graduate
students of computer science,
electrical and electronic engineering, and bioinformatics. The material covered will also be of
value to
any researcher familiar with the
basics of discrete mathematics, graph theory and
algorithms.
Contents
Announcements
I will keep errata
here for the book
on this page along with any
possible teaching aids although I can not promise to provide
any teachingmaterial in near future. If
you find any errors related
to the text,
I would be pleased if you would
let me know at erciy2001 at
yahoo dot com.
K. Erciyes