A Consistent Histogram Estimator for Exchangeable Graph Models

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Stanley Chan, Edoardo Airoldi ;
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(1):208-216, 2014.

Abstract

Exchangeable graph models (ExGM) subsume a number of popular network models. The mathematical object that characterizes an ExGM is termed a graphon. Finding scalable estimators of graphons, provably consistent, remains an open issue. In this paper, we propose a histogram estimator of a graphon that is provably consistent and numerically efficient. The proposed estimator is based on a sorting-and-smoothing (SAS) algorithm, which first sorts the empirical degree of a graph, then smooths the sorted graph using total variation minimization. The consistency of the SAS algorithm is proved by leveraging sparsity concepts from compressed sensing.

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