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- Chapter 1: Ubiquity of Networks
- 1.1. Introduction
- 1.2. Online social networking services
- 1.3. Online bibliographic services
- 1.4. Generic network models
- 1.5. Network model generators
- 1.6. A real-world network
- 1.7. Conclusions
- Chapter 2: Network Analysis
- 2.1. Conclusions and future work
- Chapter 3: Network Games
- 3.1. Game theory introduction
- 3.2. Congestion games and resource pricing
- 3.3. Cooperation in network synthesis game
- 3.4. Bayesian games
- 3.5. Applications
- 3.6. Conclusion
- Chapter 4: Balance Theory
- 4.1. Conclusion
- Chapter 5: Network Dynamics
- 5.1. Evolutionary and volatile network dynamics
- 5.2. Time graphs
- 5.3. Markov chains
- 5.4. Strategic network partnering using Markov decision processes
- 5.5. Conclusion
- Chapter 6: Diffusion and Contagion
- 6.1. Population preference spread
- 6.2. Percolation model
- 6.3. Disease epidemic models
- 6.4. Community detection
- 6.5. Community correlation versus influence
- 6.6. Conclusion
- Chapter 7: Influence Diffusion and Contagion
- 7.1. Stochastic model
- 7.2. Social learning
- 7.3. Social media influence
- 7.4. Conclusion
- Chapter 8: Power in Exchange Networks
- 8.1. Conclusion
- Chapter 9: Economic Networks
- 9.1. Network effects
- 9.2. Conclusion
- Chapter 10: Network Capital
- 10.1. Social capital used for physical capital access
- 10.2. Conclusion
- Chapter 11: Network Organizations
- 11.1. Conclusion
- Chapter 12: Emerging Trends
- 12.1. Conclusion
The emerging field of network science represents a new style of research that can unify such traditionally-diverse fields as sociology, economics, physics, biology, and computer science. It is a powerful tool in analyzing both natural and man-made systems, using the relationships between players within these networks and between the networks themselves to gain insight into the nature of each field. Until now, studies in network science have been focused on particular relationships that require varied and sometimes-incompatible datasets, which has kept it from being a truly universal discipline.
Computational Network Science seeks to unify the methods used to analyze these diverse fields. This book provides an introduction to the field of Network Science and provides the groundwork for a computational, algorithm-based approach to network and system analysis in a new and important way. This new approach would remove the need for tedious human-based analysis of different datasets and help researchers spend more time on the qualitative aspects of network science research.
- Demystifies media hype regarding Network Science and serves as a fast-paced introduction to state-of-the-art concepts and systems related to network science
- Comprehensive coverage of Network Science algorithms, methodologies, and common problems
- Includes references to formative and updated developments in the field
- Coverage spans mathematical sociology, economics, political science, and biological networks
Network researchers and graduate students; professionals in computational disciplines; researchers in most scientific, social, and cross-disciplinary fields
- No. of pages:
- © Morgan Kaufmann 2015
- 29th September 2014
- Morgan Kaufmann
- Paperback ISBN:
- eBook ISBN:
Henry Hexmoor, received an M.S. from Georgia Tech and a Ph.D. in Computer Science from the State University of New York, Buffalo in 1996. He is a long-time IEEE senior member and has taught at the University of North Carolina and the University of Arkansas. Currently, he is an associate professor with the Computer Science department at Southern Illinois University in Carbondale, IL. He has published widely in the fields of artificial intelligence and multiagent systems. His research interests include multiagent systems, artificial intelligence, cognitive science, mobile robotics, and predictive models for transportation systems.
Associate Professor, Computer Science Department, Southern Illinois University, Carbondale, Illinois
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