Vertex nomination: The canonical sampling and the extended spectral nomination schemes

Jordan Yoder, Li Chen, Henry Pao, Eric Bridgeford, Keith Levin, Donniell E. Fishkind, Carey Priebe and Vince Lyzinski

Computational Statistics & Data Analysis, 2020, vol. 145, issue C

Abstract: Suppose that one particular block in a stochastic block model is of interest, but block labels are only observed for a few of the vertices in the network. Utilizing a graph realized from the model and the observed block labels, the vertex nomination task is to order the vertices with unobserved block labels into a ranked nomination list with the goal of having an abundance of interesting vertices near the top of the list. There are vertex nomination schemes in the literature, including the optimally precise canonical nomination scheme LC and the consistent spectral partitioning nomination scheme LP. While the canonical nomination scheme LC is provably optimally precise, it is computationally intractable, being impractical to implement even on modestly sized graphs.

Keywords: Vertex nomination; Markov chain Monte Carlo; Spectral partitioning; Mclust (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:145:y:2020:i:c:s0167947320300074

DOI: 10.1016/j.csda.2020.106916

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