Inferring the structure and scale of modular networks
Source:
6th International Conference on Mining and Learning with Graphs, Helsinki, Finland (2008)
URL:
http://www.cis.hut.fi/MLG08/
Abstract:
We present an efficient, principled, and interpretable technique for
inferring module assignments and for identifying the optimal number of
modules in a given network, based on variational Bayesian inference
for stochastic block models. We show how our method extends previous
work and addresses the "resolution limit problem". We apply the
technique to synthetic and real networks.
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