Random graphs are mathematical models that have applications in a wide range of domains. We study the following model where one adds Erdos--R\'enyi %DIFDELCMD < \xspace %%% (ER) type perturbation to a random geometric graph. More precisely, assume G_X^{*} is a random geometric graph sampled from a nice measure on a metric space X = (X,d). The input observed graph G(p,q) is generated by removing each existing edge from G_X^* with probability p, while inserting each non-existent edge to G_X^{*} with probability q. We refer to such random p-deletion and q-insertion as ER-perturbation. Although these graphs are related to the objects in the continuum percolation theory, our understanding of them is still rather limited. In this paper we consider a localized version of the classical notion of clique number for the aforementioned ER-perturbed random geometric graphs : Specifically, we study the edge clique number for each edge in a graph, defined as the size of the largest clique(s) in the graph containing that edge. The clique number of the graph is simply the largest edge clique number. Interestingly, given a ER-perturbed random geometric graph, we show that the edge clique number presents two fundamentally different types of behaviors, depending on which "type" of randomness it is generated from. As an application of the above results, we show that by using a filtering process based on the edge clique number, we can recover the shortest-path metric of the random geometric graph G_X^* within a multiplicative factor of 3, from an ER-perturbed observed graph G(p,q), for a significantly wider range of insertion probability q than in previous work.
Random graphs are mathematical models that have applications in a wide range of domains. We study the following model where one adds Erdos--R\'enyi %DIFDELCMD < \xspace %%% (ER) type perturbation to a random geometric graph. More precisely, assume G_X^{*} is a random geometric graph sampled from a nice measure on a metric space X = (X,d). The input observed graph G(p,q) is generated by removing each existing edge from G_X^* with probability p, while inserting each non-existent edge to G_X^{*} with probability q. We refer to such random p-deletion and q-insertion as ER-perturbation. Although these graphs are related to the objects in the continuum percolation theory, our understanding of them is still rather limited. In this paper we consider a localized version of the classical notion of clique number for the aforementioned ER-perturbed random geometric graphs : Specifically, we study the edge clique number for each edge in a graph, defined as the size of the largest clique(s) in the graph containing that edge. The clique number of the graph is simply the largest edge clique number. Interestingly, given a ER-perturbed random geometric graph, we show that the edge clique number presents two fundamentally different types of behaviors, depending on which "type" of randomness it is generated from. As an application of the above results, we show that by using a filtering process based on the edge clique number, we can recover the shortest-path metric of the random geometric graph G_X^* within a multiplicative factor of 3, from an ER-perturbed observed graph G(p,q), for a significantly wider range of insertion probability q than in previous work.
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ER-perturbed random geometric graphs
ER-perturbed random geometric graphs
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edge clique number
edge clique number