This unique textbook/reference presents unified coverage of bioinformatics topics relating to both biological sequences and biological networks, providing an in-depth analysis of cutting-edge distributed algorithms, as well as of relevant sequential algorithms.
Focusing on distributed graph algorithms, this book offers an in-depth exploration tailored for computer network applications. It covers essential topics such as spanning trees, routing algorithms, and self-stabilization, while also delving into graph-theoretical distributed approximation algorithms relevant to ad hoc wireless networks. Each algorithm is thoroughly implemented with practical examples, and key concepts like dominating sets and node mobility are examined. Additionally, a simple simulator is included, along with practical exercises at the end of each chapter to reinforce learning.
A Practical Guide Using Python
This textbook discusses the design and implementation of basic algebraic graph algorithms, and algebraic graph algorithms for complex networks, employing matroids whenever possible. The text describes the design of a simple parallel matrix algorithm kernel that can be used for parallel processing of algebraic graph algorithms.