  Real-data networks often appear to have strong modularity, or
network-of-networks structure, in which subgraphs of various size and
consistency occur. Finding the respective subgraph structure is of great
importance, in particular for understanding the dynamics on these networks.
Here we study modular networks using generalized method of maximum likelihood.
We first demonstrate how the method works on computer-generated networks with
the subgraphs of controlled connection strengths and clustering. We then
implement the algorithm which is based on weights of links and show its
efficiency in finding weighted subgraphs on fully connected graph and on
real-data network of yeast.
