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Network histograms and universality of blockmodel approximation
Sofia C. Olhede and Patrick J. Wolfe
PNAS 111(41):14722-14727
Stochastic block model
From Guimerà and Sales-Pardo (2009) PNAS 106(52):22073-78
a generative model for graphs with heterogenous degrees.
often used as model for learning community structure.
can predict missing edges in the network
Key concepts
A graphon is a continuous 2D probability density function for interactions between nodes.
The structure of any network can be described by its number of nodes n and an appropriate graphon.
Describing networks using histograms
Instead of learning a block model, we will look for a histogram approximation of the graphon that best fits the data.
The authors provide an error metric to support a maximum likelihood estimation of the best bin width to choose.
Code is provided!https://github.com/p-wolfe/network-histogram-code