This self-contained book presents theoretical results and current applications of structural network analysis to synthetic and real-world networks. The individual chapters are a careful balance of classical and novel approaches, ranging in scope from determining the structural information of networks, partitions, and distances in graphs to applying structural graph analysis to biological networks.A major goal of the book is to highlight the need for theoretical and applied results to enhance knowledge and understanding of networks, in general, and to solve challenging practical problems. Another major theme of the work is to show that very large and complex data sets--for example, in biology or chemistry--require novel approaches to take into account the size and complexity of such networks under investigation.Key features:* Each chapter is written comprehensively, focusing not only on the presentation of a method or an algorithm, but also providing additional background information on graph theory, computer science, machine learning, statistics, or biology.* Emphasis is not only on mathematical methods; the applications presented demonstrate the potential for each method examined.* Unique in the literature: no existing book surveys such a broad range of mathematical methods for structurally analyzing networks.* Gathers information and topics that are scattered throughout journal articles.* May be used as a supplementary textbook in graduate-level seminars on structural graph analysis or network-based machine learning methods.Structural Analysis of Complex Networks is suitable for an interdisciplinary readership of researchers, practitioners, and graduate students working in pure and applied discrete mathematics, statistical data analysis, computer science, machine learning, artificial intelligence, bioinformatics, computational biology, web or text mining, and related disciplines.