Presented by:
David Choi
Date:
Friday 15th July 2016 - 15:30 to 16:00
Venue:
INI Seminar Room 1
Abstract:
Theoretical results are becoming known for community detection and clustering
of networks; however, these results assume an idealized generative model that is
unlikely to hold in many settings. Here we consider exploratory co-clustering of
a bipartite network, where the rows and columns of the adjacency matrix are
assumed to be samples from an arbitrary population. This is equivalent to
assuming that the data is generated from a nonparametric model known as a
graphon. We show that co-clusters found by any method can be extended to the row
and column populations, or equivalently that the estimated blockmodel
approximates a blocked version of the generative graphon, with generalization
error bounded by n^{-1/2}. Analogous results are also shown for degree-corrected
co-blockmodels and random dot product bipartite graphs, with error rates
depending on the dimensionality of the latent variable space.
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