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.
The Up-to-Date Guide to Complex Networks for Students, Researchers, and Practitioners
Networks with complex and irregular connectivity patterns appear in biology, chemistry, communications, social networks, transportation systems, power grids, the Internet, and many big data applications. Complex Networks offers a novel engineering perspective on these networks, focusing on their key communications, networking, and signal processing dimensions.
Three leading researchers draw on recent advances to illuminate the design and characterization of complex computer networks and graph signal processing systems. The authors cover both the fundamental concepts underlying graph theory and complex networks, as well as current theory and research. They discuss spectra and signal processing in complex networks, graph signal processing approaches for extracting information from structural data, and advanced techniques for multiscale analysis.
- What makes networks complex, and how to successfully characterize them
- Graph theory foundations, definitions, and concepts
- Full chapters on small-world, scale-free, small-world wireless mesh, and small-world wireless sensor networks
- Complex network spectra and graph signal processing concepts and techniques
- Multiscale analysis via transforms and wavelets