Dear Tiago,

Thank you very much for creating and maintaining this awesome package! I really enjoy using it.

I have a question regarding finding the NestedBlockState with minimum description length.

I currently have a very large bipartite network with approximately 300k edges and 20k nodes, say, g.

1. founding the initial state using an agglomerative heuristic. state = gt.minimize_nested_blockmodel_dl(g, deg_corr=True)

2. equilibrate the Markov chain at beta = 1 bs = state.get_bs() bs += [np.zeros(1)] * (nL - len(bs)) state = state.copy(bs=bs, sampling=True) gt.mcmc_equilibrate(state, wait=1000, epsilon=1e-6, mcmc_args=dict(niter=10))

3. equilibrate the Markov chain at beta = numpy.inf gt.mcmc_equilibrate(state, wait=1000, epsilon=1e-6, mcmc_args=dict(beta = numpy.inf, niter=10))

4. founding the state with minimum description length gt.mcmc_equilibrate(state, wait=200, epsilon=1e-6, mcmc_args=dict(beta = numpy.inf, niter=10), callback = finding_minState) where finding_minState is a function that checks whether the state is the minimum MDL found so far.

5. to save the state to pickle files, I use the following to construct a new NestedBlockState minState = gt.NestedBlockState(g, bs = state.get_bs(), state_args = state.state_args) where state_args is some partition prior imposed on the graph.

In the above, to speed up the inference, I chose epsilon to be 1e-6 or even 1e-2. I know I can also speed up the program by passing multiflip=True, but since I ran the program on the school server. It currently only have 2.27 available.

I run the above code for multiple times to study the computational cost and convergence behavior. For each run, I also calculate the average description length over MCMC sweeps.

My question is:

Is the above approach correct to find the state with minimum description length?

Looking forward to your reply.

Best, Terry

-- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/