Guide to Graph Algorithms

Sequential, Parallel and Distributed

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




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