A random walk model for infection on graphs: spread of epidemics & rumours with mobile agents
Discrete Event Dynamic Systems
Viral processes by random walks on random regular graphs
APPROX'11/RANDOM'11 Proceedings of the 14th international workshop and 15th international conference on Approximation, randomization, and combinatorial optimization: algorithms and techniques
Availability in large networks: global characteristics from local unreliability properties
MMB'12/DFT'12 Proceedings of the 16th international GI/ITG conference on Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance
Review of statistical network analysis: models, algorithms, and software
Statistical Analysis and Data Mining
Farout vertices in weighted repeated configuration model
ACM SIGMETRICS Performance Evaluation Review
Relevance of SIR Model for Real-world Spreading Phenomena: Experiments on a Large-scale P2P System
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Potential networks, contagious communities, and understanding social network structure
Proceedings of the 22nd international conference on World Wide Web
Active learning and inference method for within network classification
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Optimal content placement for peer-to-peer video-on-demand systems
IEEE/ACM Transactions on Networking (TON)
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Information propagation through peer-to-peer systems, online social systems, wireless mobile ad hoc networks and other modern structures can be modelled as an epidemic on a network of contacts. Understanding how epidemic processes interact with network topology allows us to predict ultimate course, understand phase transitions and develop strategies to control and optimise dissemination. This book is a concise introduction for applied mathematicians and computer scientists to basic models, analytical tools and mathematical and algorithmic results. Mathematical tools introduced include coupling methods, Poisson approximation (the Stein-Chen method), concentration inequalities (Chernoff bounds and Azuma-Hoeffding inequality) and branching processes. The authors examine the small-world phenomenon, preferential attachment, as well as classical epidemics. Each chapter ends with pointers to the wider literature. An ideal accompaniment for graduate courses, this book is also for researchers (statistical physicists, biologists, social scientists) who need an efficient guide to modern approaches to epidemic modelling on networks.