Network Science: Theory and Applications
A comprehensive look at the emerging science of networks
Network science helps you design faster, more resilient communication networks; revise infrastructure systems such as electrical power grids, telecommunications networks, and airline routes; model market dynamics; understand synchronization in biological systems; and analyze social interactions among people.
This is the first book to take a comprehensive look at this emerging science. It examines the various kinds of networks (regular, random, small-world, influence, scale-free, and social) and applies network processes and behaviors to emergence, epidemics, synchrony, and risk. The book's uniqueness lies in its integration of concepts across computer science, biology, physics, social network analysis, economics, and marketing.
The book is divided into easy-to-understand topical chapters and the presentation is augmented with clear illustrations, problems and answers, examples, applications, tutorials, and a discussion of related Java software. Chapters cover:
This book offers a new understanding and interpretation of the field of network science. It is an indispensable resource for researchers, professionals, and technicians in engineering, computing, and biology. It also serves as a valuable textbook for advanced undergraduate and graduate courses in related fields of study.
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The author is a pioneer of network science as a modeling activity that combines complex adaptive systems, chaos, and mean-field theory. This text is dense mathematically and includes Java code. There are thirteen chapters with exercises, a bibliography, and index. The history of significant events is outlined from Euler’s Bridges of Konigsberg in 1736 to Gabbay in 2007. Topics include structure, emergence, dynamism, autonomy, bottom-up evolution, topology, power, and stability. Graph theory describes properties, matrix representation, classes, modeling and simulation. Regular networks are constructed by a generative procedure. Network-centric organizations reduce links and path lengths to lower costs and latency. A new metric, link efficiency, compares network types. Entropy initially increases as nodes are added, flattens, then diminishes to zero as structure predominates. Networks have topological phase transitions as rewiring probability increases. Network emergence describes macroscale properties resulting from microscale rules. Hub emergence is not scale-free. Cluster emergence is not small world. Feedback-loop, adaptive or environmental ermergence connects the next state to input microrules on goal-oriented networks. A network epidemic, characterized by spectral radius, propagates state or condition via links, as do antigen countermeasures which use superspreaders to decrease time and peak incidences. The classic is the Kermack-McKendrick model from 1927. Networks which follow Kirchhoff’s first law are shown where commodity flow in and out is equal. Influence networks are great models of social networks where nodes are actors. Network vulnerability is the probability that an attempted attack will succeed. Strategies such as linear are good defender and exponential for attacker. Risk is reduction of vulnerability or consequence. Resilience is defined for links, where small-world has highest followed by random then scale-free, as well as for stability, and flows where expected flow is availability times actual flow. Percolation adds links, depercolation removes them. Game theory assumes independent success probabilities. The attacker-defender problem is asymmetric. Netgain is a property where nodes compete for value proposition such as preferential attachment. Multiproduct emergence shows how shakeouts and monopolies can occur. Other market emergence types include nascent, creative destructive, or merger and acquisition. Network science can be used to model metabolism. Biology includes protein expression using Boolean networks. Chemistry uses bounded mass kinetic networks. Readers interested in quantum mechanics would seek additional sources.