Guide to Graph Algorithms: Sequential, Parallel and Distributed Second Edition
 

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, approximation algorithms and heuristics for such problems and implementation of advanced graph structures in machine learning. 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