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arXiv · 1707.03487

Robust Estimation from Multiple Graphs under Gross Error Contamination

Runze Tang·Minh Tang·Joshua T. Vogelstein·Carey E. Priebe
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Abstract

Estimation of graph parameters based on a collection of graphs is essential for a wide range of graph inference tasks. In practice, weighted graphs are generally observed with edge contamination. We consider a weighted latent position graph model contaminated via an edge weight gross error model and propose an estimation methodology based on robust Lq estimation followed by low-rank adjacency spectral decomposition. We demonstrate that, under appropriate conditions, our estimator both maintains Lq robustness and wins the bias-variance tradeoff by exploiting low-rank graph structure. We illustrate the improvement offered by our estimator via both simulations and a human connectome data experiment.

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Runze Tang, Minh Tang, Joshua T. Vogelstein, Carey E. Priebe. 2017-07-11. Robust Estimation from Multiple Graphs under Gross Error Contamination. https://arxiv.org/abs/1707.03487

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