Advances in complex networks and graph signal processing have important implications in fields ranging from communications and social networking to big data and biology. In Complex Networks, three pioneering researchers offer balanced, up-to-date coverage that will be ideal for advanced undergraduates, graduate students, researchers, and industry practitioners alike.
The authors begin by introducing the fundamental concepts underlying graph theory and complex networks. Next, they illuminate current theory and research in random networks, small-world networks, scale-free networks, and both small-world wireless mesh and sensor networks. Readers will find full chapters on spectra and signal processing in complex networks, as well as detailed introductions to graph signal processing approaches for extracting information from structural data, as well as advanced multiscale analysis techniques.
To promote deeper learning and mastery, the book contains 100+ examples, 200+ figures, and 20+ comparison tables. Each chapter includes problems as well as a section describing open research issues in the field. Appendices provide valuable reference information about vectors, matrices, anchor points, classical multiscale analysis, asymptotic behavior of functions, and additional resources for students and researchers in the field.