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SIAM Journal on Computing
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The Node Distribution of the Random Waypoint Mobility Model for Wireless Ad Hoc Networks
IEEE Transactions on Mobile Computing
The random trip model: stability, stationary regime, and perfect simulation
IEEE/ACM Transactions on Networking (TON)
Understanding the simulation of mobility models with Palm calculus
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How to Explore a Fast-Changing World (Cover Time of a Simple Random Walk on Evolving Graphs)
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Broadcasting in dynamic radio networks
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Parsimonious flooding in dynamic graphs
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Tight bounds on information dissemination in sparse mobile networks
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Information Spreading in Stationary Markovian Evolving Graphs
IEEE Transactions on Parallel and Distributed Systems
Flooding Time of Edge-Markovian Evolving Graphs
SIAM Journal on Discrete Mathematics
Information dissemination via random walks in d-dimensional space
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Mobile geometric graphs: detection, coverage and percolation
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Fast distributed computation in dynamic networks via random walks
DISC'12 Proceedings of the 26th international conference on Distributed Computing
Lower bounds on information dissemination in dynamic networks
DISC'12 Proceedings of the 26th international conference on Distributed Computing
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We present a general approach to study the flooding time (a measure of how fast information spreads) in dynamic graphs (graphs whose topology changes with time according to a random process). We consider arbitrary ergodic Markovian dynamic graph process, that is, processes in which the topology of the graph at time t depends only on its topology at time t-1 and which have a unique stationary distribution. The most well studied models of dynamic graphs are all Markovian and ergodic. Under general conditions, we bound the flooding time in terms of the mixing time of the dynamic graph process. We recover, as special cases of our result, bounds on the flooding time for the random trip model and the random path models; previous analysis techniques provided bounds only in restricted settings for such models. Our result also provides the first bound for the random waypoint model (which is tight for the most realistic ranges of network parameters) whose analysis had been an important open question.