Network histograms and universality of blockmodel approximation Sofia C. Olhede and Patrick J. Wolfe...
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Transcript of Network histograms and universality of blockmodel approximation Sofia C. Olhede and Patrick J. Wolfe...
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