Networks constitute the backbone of complex systems, from the human brain to computer communications, transport infrastructures to online social systems and metabolic reactions to financial markets. Characterising their structure improves our understanding of the physical, biological, economic and social phenomena that shape our world. Rigorous and thorough, this textbook presents a detailed overview of the new theory and methods of network science. Covering algorithms for graph exploration, node ranking and network generation, among the others, the book allows students to experiment with network models and real-world data sets, providing them with a deep understanding of the basics of network theory and its practical applications. Systems of growing complexity are examined in detail, challenging students to increase their level of skill. An engaging presentation of the important principles of network science makes this the perfect reference for researchers and undergraduate and graduate students in physics, mathematics, engineering, biology, neuroscience and the social sciences. > The first textbook integrating all aspects of network science, from fundamental principles to mathematical analysis and computational modelling > Provides a comprehensive selection of data sets of social, biological and technological complex networks > Includes detailed descriptions of computer algorithms for network analysis and modelling with corresponding implementations in C language freely available online > Presents the history of network science alongside the corresponding concepts and mathematical tools, by combining theory with the real-world applications that have inspired network models and algorithms Table of Contents Preface Introduction 1. Graphs and graph theory 2. Centrality measures 3. Random graphs 4. Small-world networks 5. Generalised random graphs 6. Models of growing graphs 7. Degree correlations 8. Cycles and motifs 9. Community structure 10. Weighted networks Appendix References Author index Index.