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