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Computational Network Science

Book Description

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

Table of Contents

  1. Cover
  2. Title page
  3. Table of Contents
  4. Copyright Page
  5. Preface
  6. Chapter 1: Ubiquity of Networks
    1. Abstract
    2. 1.1. Introduction
    3. 1.2. Online social networking services
    4. 1.3. Online bibliographic services
    5. 1.4. Generic network models
    6. 1.5. Network model generators
    7. 1.6. A real-world network
    8. 1.7. Conclusions
  7. Chapter 2: Network Analysis
    1. Abstract
    2. 2.1. Conclusions and future work
  8. Chapter 3: Network Games
    1. Abstract
    2. 3.1. Game theory introduction
    3. 3.2. Congestion games and resource pricing
    4. 3.3. Cooperation in network synthesis game
    5. 3.4. Bayesian games
    6. 3.5. Applications
    7. 3.6. Conclusion
  9. Chapter 4: Balance Theory
    1. Abstract
    2. 4.1. Conclusion
  10. Chapter 5: Network Dynamics
    1. Abstract
    2. 5.1. Evolutionary and volatile network dynamics
    3. 5.2. Time graphs
    4. 5.3. Markov chains
    5. 5.4. Strategic network partnering using Markov decision processes
    6. 5.5. Conclusion
  11. Chapter 6: Diffusion and Contagion
    1. Abstract
    2. 6.1. Population preference spread
    3. 6.2. Percolation model
    4. 6.3. Disease epidemic models
    5. 6.4. Community detection
    6. 6.5. Community correlation versus influence
    7. 6.6. Conclusion
  12. Chapter 7: Influence Diffusion and Contagion
    1. Abstract
    2. 7.1. Stochastic model
    3. 7.2. Social learning
    4. 7.3. Social media influence
    5. 7.4. Conclusion
  13. Chapter 8: Power in Exchange Networks
    1. Abstract
    2. 8.1. Conclusion
  14. Chapter 9: Economic Networks
    1. Abstract
    2. 9.1. Network effects
    3. 9.2. Conclusion
  15. Chapter 10: Network Capital
    1. Abstract
    2. 10.1. Social capital used for physical capital access
    3. 10.2. Conclusion
  16. Chapter 11: Network Organizations
    1. Abstract
    2. 11.1. Conclusion
  17. Chapter 12: Emerging Trends
    1. Abstract
    2. 12.1. Conclusion
  18. Appendix