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On the Propagation of Low-Rate Measurement Error to Subgraph Counts in Large Networks

Prakash Balach, ran, Eric D. Kolaczyk, Weston D. Viles; 18(61):1−33, 2017.


Our work in this paper is inspired by a statistical observation that is both elementary and broadly relevant to network analysis in practice---that the uncertainty in approximating some true graph $G=(V,E)$ by some estimated graph $\hat{G}=(V,\hat{E})$ manifests as errors in our knowledge of the presence/absence of edges between vertex pairs, which must necessarily propagate to any estimates of network summaries $\eta(G)$ we seek. Motivated by the common practice of using plug-in estimates $\eta(\hat{G})$ as proxies for $\eta(G)$, our focus is on the problem of characterizing the distribution of the discrepancy $D=\eta(\hat{G}) - \eta(G)$, in the case where $\eta(\cdot)$ is a subgraph count. Specifically, we study the fundamental case where the statistic of interest is $|E|$, the number of edges in $G$. Our primary contribution in this paper is to show that in the empirically relevant setting of large graphs with low-rate measurement errors, the distribution of $D_E=|\hat{E}| - |E|$ is well-characterized by a Skellam distribution, when the errors are independent or weakly dependent. Under an assumption of independent errors, we are able to further show conditions under which this characterization is strictly better than that of an appropriate normal distribution. These results derive from our formulation of a general result, quantifying the accuracy with which the difference of two sums of dependent Bernoulli random variables may be approximated by the difference of two independent Poisson random variables, i.e., by a Skellam distribution. This general result is developed through the use of Stein's method, and may be of some general interest. We finish with a discussion of possible extension of our work to subgraph counts $\eta(G)$ of higher order.

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