Principles Of Random Walk

By A. Mueller. 2016

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Random walks and diffusion on networks (2016, background): “, trees in which each node has the same number of neighboring nodes, which we henceforth call the node “degree”) [4, 29–35].”
Local Convergence of Large Critical Multi-type Galton–Watson Trees and Applications to Random Maps (2014, methodology): “As a consequence of this, we can apply Spitzer’s strong ratio theorem (see [29], p.”
Random Walks and Heat Kernels on Graphs (2017, background): “In the case where the random walk is on Z, there is a rich theory (see [8,14]) whose main message is that under diffusive scaling (multiplying by 1 √ n at time n) the random walk converges to a Gaussian distribution.”
Sybil Attacks and Their Defenses in the Internet of Things (2014, methodology): “Using the random walk algorithm [52], the Sybil or honest nodes can be determined, which is similar to SybilGuard.”
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal (2022, background): “Random walk [111], [112], [113], [114] inferences have been widely investigated in this type of model.”
Local limits of conditioned Galton-Watson trees: the infinite spine case (2013, background): “has period 1), the strong ratio theorem for recurrent aperiodic random walks, see Theorem T1 p49 of [25], gives that, for l ∈ Z:”