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Graph shotgun assembly refers to the problem of reconstructing a graph from a collection of local neighborhoods. In this paper, we consider shotgun assembly of ErdH{o}s-Renyi random graphs $G(n, p_n)$, where $p_n = n^{-alpha}$ for $0 < alpha < 1$. We consider both reconstruction up to isomorphism as well as exact reconstruction (recovering the vertex labels as well as the structure). We show that given the collection of distance-$1$ neighborhoods, $G$ is exactly reconstructable for $0 < alpha < frac{1}{3}$, but not reconstructable for $frac{1}{2} < alpha < 1$. Given the collection of distance-$2$ neighborhoods, $G$ is exactly reconstructable for $0 < alpha < frac{1}{2}$, but not reconstructable for $frac{3}{4} < alpha < 1$.
Given an unlabeled graph $G$ on $n$ vertices, let ${N_{G}(v)}_{v}$ be the collection of subgraphs of $G$, where for each vertex $v$ of $G$, $N_{G}(v)$ is the subgraph of $G$ induced by vertices of $G$ of distance at most one from $v$. We show that th
Through Monte Carlo Simulation, the well-known majority-vote model has been studied with noise on directed random graphs. In order to characterize completely the observed order-disorder phase transition, the critical noise parameter $q_c$, as well as
Reaction networks are commonly used within the mathematical biology and mathematical chemistry communities to model the dynamics of interacting species. These models differ from the typical graphs found in random graph theory since their vertices are
Let $A in mathbb{R}^{n times n}$ be the adjacency matrix of an ErdH{o}s Renyi graph $G(n, d/n)$ for $d = omega(1)$ and $d leq 3log(n)$. We show that as $n$ goes to infinity, with probability that goes to $1$, the adjacency matrix of the $3$-core of $G(n, d/n)$ is invertible.
Using Monte Carlo simulations we study the Ising model with spin S=1/2 and 1 on {it directed} and {it undirected} Erdos-Renyi (ER) random graphs, with $z$ neighbors for each spin. In the case with spin S=1/2, the {it undirected} and {it directed} ER