Performance Analysis of Complex Networks and Systems

1. Introduction Part I. Probability Theory: 2. Random variables 3. Basic distributions 4. Correlation 5. Inequalities 6. Limit laws Part II. Stochastic Processes: 7. The Poisson process 8. Renewal theory 9. Discrete-time Markov chains 10. Continuous-time Markov chains 11. Applications of Markov chains 12. Branching processes 13. General queueing theory 14. Queueing models Part III. Network Science: 15. General characteristics of graphs 16. The shortest path problem 17. Epidemics in networks 18. The efficiency of multicast 19. The hopcount and weight to an anycast group Appendix A. A summary of matrix theory Appendix B. Solutions to problems.

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