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Register a new userStochastic rumors on random trees
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Added by
Pablo Martin Rodriguez
Publication date
2021
fields
Physics
and research's language is
English
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The Maki-Thompson rumor model is defined by assuming that a population represented by a graph is subdivided into three classes of individuals; namely, ignorants, spreaders and stiflers. A spreader tells the rumor to any of its nearest ignorant neighbors at rate one. At the same rate, a spreader becomes a stifler after a contact with other nearest neighbor spreaders, or stiflers. In this work we study the model on random trees. As usual we define a critical parameter of the model as the critical value around which the rumor either becomes extinct almost-surely or survives with positive probability. We analyze the existence of phase-transition regarding the survival of the rumor, and we obtain estimates for the mean range of the rumor. The applicability of our results is illustrated with examples on random trees generated from some well-known discrete distributions.
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We address questions of logic and expressibility in the context of random rooted trees. Infiniteness of a rooted tree is not expressible as a first order sentence, but is expressible as an existential monadic second order sentence (EMSO). On the other hand, finiteness is not expressible as an EMSO. For a broad class of random tree models, including Galton-Watson trees with offspring distributions that have full support, we prove the stronger statement that finiteness does not agree up to a null set with any EMSO. We construct a finite tree and a non-null set of infinite trees that cannot be distinguished from each other by any EMSO of given parameters. This is proved via set-pebble Ehrenfeucht games (where an initial colouring round is followed by a given number of pebble rounds).
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We consider the free boundary condition Gibbs measure of the Potts model on a random tree. We provide an explicit temperature interval below the ferromagnetic transition temperature for which this measure is extremal, improving older bounds of Mossel and Peres. In information theoretic language extremality of the Gibbs measure corresponds to non-reconstructability for symmetric q-ary channels. The bounds are optimal for the Ising model and appear to be close to what we conjecture to be the true values up to a factor of 0.0150 in the case q = 3 and 0.0365 for q = 4. Our proof uses an iteration of random boundary entropies from the outside of the tree to the inside, along with a symmetrization argument.
For a directed graph $G(V_n, E_n)$ on the vertices $V_n = {1,2, dots, n}$, we study the distribution of a Markov chain ${ {bf R}^{(k)}: k geq 0}$ on $mathbb{R}^n$ such that the $i$th component of ${bf R}^{(k)}$, denoted $R_i^{(k)}$, corresponds to the value of the process on vertex $i$ at time $k$. We focus on processes ${ {bf R}^{(k)}: k geq 0}$ where the value of $R_i^{(k+1)}$ depends only on the values ${ R_j^{(k)}: j to i}$ of its inbound neighbors, and possibly on vertex attributes. We then show that, provided $G(V_n, E_n)$ converges in the local weak sense to a marked Galton-Watson process, the dynamics of the process for a uniformly chosen vertex in $V_n$ can be coupled, for any fixed $k$, to a process ${ mathcal{R}_emptyset^{(r)}: 0 leq r leq k}$ constructed on the limiting marked Galton-Watson tree. Moreover, we derive sufficient conditions under which $mathcal{R}^{(k)}_emptyset$ converges, as $k to infty$, to a random variable $mathcal{R}^*$ that can be characterized in terms of the attracting endogenous solution to a branching distributional fixed-point equation. Our framework can also be applied to processes ${ {bf R}^{(k)}: k geq 0}$ whose only source of randomness comes from the realization of the graph $G(V_n, E_n)$.
We investigate the effective resistance $R_n$ and conductance $C_n$ between the root and leaves of a binary tree of height $n$. In this electrical network, the resistance of each edge $e$ at distance $d$ from the root is defined by $r_e=2^dX_e$ where the $X_e$ are i.i.d. positive random variables bounded away from zero and infinity. It is shown that $mathbf{E}R_n=nmathbf{E}X_e-(operatorname {mathbf{Var}}(X_e)/mathbf{E}X_e)ln n+O(1)$ and $operatorname {mathbf{Var}}(R_n)=O(1)$. Moreover, we establish sub-Gaussian tail bounds for $R_n$. We also discuss some possible extensions to supercritical Galton--Watson trees.
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