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C++ implementation of a MCMC sampler for the (microcanonical) bipartite SBM
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README.md

bipartiteSBM-MCMC PRs Welcome Build Status

bipartiteSBM-MCMC is a MCMC sampler for the degree-corrected bipartite Stochastic Block Model. Three sampling procedures are provided. In marginalize and maximize mode, the group memberships of each node are found assuming we know the number of communities, (Ka, Kb), of the system. In estimate mode, one samples the posterior distribution directly assuming we do not know the number of groups.

It is also used as a submodule for the det_k_bisbm library.

Table of content

Usage

Compilation

This code requires compilers that support C++11 features. It also depends on boost::program_options and cmake.

Compilation:

cmake .
make

The binaries are built in bin/.

Options:

bin/mcmc  

with no options will print usage.

All useful outputs are directed to stdout.

Examples

Example marginalization

In the marginalization mode, one samples a pool of equilibrated Markov chain configurations, compute the marginal distribution (over possible community labels) of each node, and finally returns the configuration by assigning each node to its maximal possible community label.

bin/mcmc -e <edge_list_path> -n <block_sizes> -b <burn_in_steps> -t <sampling_steps> -x <steps_await> -y <block_types> -z <ka> <kb> -f <sampling_frequency> -E <epsilon> --randomize --membership_path <optional_membership_file>
  • REQUIRED:

    -e <edge_list_path> – path to the edgelist file; formatted as explained.

    -n <block_sizes> – block sizes vector (optional if --membership_path is specified).

    -b <burn_in_steps> – number of burn-in steps.

    -t <sampling_steps> – number of sweeps in the simulated annealing process.

    -x <steps_await> – number of accumulated steps before the stop of algorithm when the max/min likelihood shows no change.

    -y <block_types> – block types vector. Note that the node indexes should be ordered that type-a starts first, and then type-b the second.

    -z <ka> <kb> – number of type-a and type-b communities (optional if --membership_path is specified).

  • OPTIONAL:

    -f <sampling_frequency> – number of steps between each sample

    -E <epsilon> – the epsilon parameter for more efficient MCMC sampling on SBM.

    --randomize – shuffle the community labels of each node.

    --membership_path <optional_membership_file> – initial node membership configuration for seeding the Markov chain.

Example call (marginalization):

bin/mcmc -e dataset/bisbm-n_1000-ka_4-kb_6-r-1.0-Ka_30-Ir_1.75.gt.edgelist -n 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 -t 20000 -x 100 -b 1000 -y 500 500 -z 10 10 -f 10 > marginalization_result.txt

The output is sent to stdout thus via passing a pipe > to a file path, one could work further on the result.

  • OUTPUT:

    0 0 1 1 0 0 1 0 1 1 1 0 1 0 0 1 0 1 1 0 1 0 1 1 0 0 0 ...

The output is an instance of Monte Carlo samples. Each column represents the community label of each node.

Example maximization

In the maximization mode, we guess the planted partition by maximizing the likelihood of the partition (with simulated annealing). There are no burn-in process and no <sampling_frequency> if our goal is to find the maximal likelihood configuration.

bin/mcmc -e <edge_list_path> -n <block_sizes> -t <sampling_steps> -x <steps_await> -y <block_types> -z <ka> <kb> --maximize -c <cooling_schedule> -a <param_1> <param_2> --randomize  -E <epsilon> --membership_path <optional_membership_file>
  • REQUIRED:

    -e <edge_list_path> – path to the edgelist file; formatted as explained.

    -n <block_sizes> – block sizes vector (optional if --membership_path is specified).

    -t <sampling_steps> – number of sweeps in the simulated annealing process.

    -x <steps_await> – number of accumulated steps before the stop of algorithm when the max/min likelihood shows no change.

    -y <block_types> – block types vector. Note that the node indexes should be ordered that type-a starts first, and then type-b the second.

    -z <ka> <kb> – number of type-a and type-b communities (optional if --membership_path is specified).

    --maximize – maximization mode.

    -c <cooling_schedule>, and -a <param_1> <param_2> – see cooling schedule specification.

  • OPTIONAL:

    --randomize – shuffle the community labels of each node.

    -E <epsilon> – the epsilon parameter for more efficient MCMC sampling on SBM.

    --membership_path <optional_membership_file> – initial node membership configuration for seeding the Markov chain.

Example call (maximization):

The call is similar to that of the marginalization mode:

bin/mcmc -e dataset/southernWomen.edgelist -n 4 4 4 3 3 3 3 3 3 2 -t 1000 -x 100 --maximize -c exponential -a 10 0.1 -y 18 14 -z 5 5 -E 0.001 --randomize
  • OUTPUT:

    4 4 4 4 4 4 2 0 2 1 3 3 1 1 1 0 2 2 6 6 6 6 6 8 5 8 8 9 5 7 7 7

Same as marginalization, the output is an instance of Monte Carlo samples. Each column represents the community label of each node.

Example estimation

In the estimation mode, one could use the Markov chain Monte Carlo algorithm to sample the posterior distribution directly. There are two complementary options for the sampling, one is via passing --uni --estimate, the other is via passing --estimate. The prior mode implements the algorithm outlined in the paper by Maria A. Riolo et al; the latter model implements the algorithm proposed in our paper.

bin/mcmc_history -e <edge_list_path> -n <block_sizes> -t <sampling_steps> -y <block_types> -z <ka> <kb> -f <sampling_frequency> --randomize
  • REQUIRED:

    -e <edge_list_path> – path to the edgelist file; formatted as explained.

    -n <block_sizes> – block sizes vector (optional if --membership_path is specified).

    -b <burn_in_steps> – number of burn-in steps.

    -t <sampling_steps> – number of sweeps in the simulated annealing process.

    -y <block_types> – block types vector. Note that the node indexes should be ordered that type-a starts first, and then type-b the second.

    -z <ka> <kb> – number of type-a and type-b communities (optional if --membership_path is specified).

    --estimate – mode for estimating the number of communities.

  • OPTIONAL:

    --randomize – shuffle the community labels of each node.

    -f <sampling_frequency> – number of steps between each sample

    --uni – whether bipartite structure is strictly adhered.

    --membership_path <optional_membership_file> – initial node membership configuration for seeding the Markov chain.

Example call (estimating the 2D posterior):

bin/mcmc_history -e dataset/bisbm-n_1000-ka_4-kb_6-r-1.0-Ka_30-Ir_1.75.gt.edgelist -n 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 -t 100000 -x 10000 -y 500 500 -z 10 10 --randomize --estimate

Example call (estimating the 1D posterior):

bin/mcmc_history -e dataset/bisbm-n_1000-ka_4-kb_6-r-1.0-Ka_30-Ir_1.75.gt.edgelist -n 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 -t 100000 -x 10000 -y 500 500 -z 10 10 --randomize --estimate --uni

Example call (estimating the 2D posterior with user-defined initial condition):

One could pass a file containing the communities of the nodes (e.g. <optional_membership_file.txt>) and initialize the MCMC chain via this starting configuration. When this is the case, -n and -z flag are not needed.

time bin/mcmc_history -e ../../dataset/bisbm-n_1000-ka_4-kb_6-r-1.0-Ka_30-Ir_1.75.gt.edgelist -t 10000 -x 10000 -y 500 500 --randomize --estimate --membership_path dataset/optional_membership_file.txt

Example call (estimating the 1D posterior with user-defined initial condition):

Or, if one does not specifically target a bipartite structure:

time bin/mcmc_history -e ../../dataset/bisbm-n_1000-ka_4-kb_6-r-1.0-Ka_30-Ir_1.75.gt.edgelist -t 10000 -x 10000 -y 500 500 --randomize --estimate --membership_path dataset/optional_membership_file.txt --uni
  • OUTPUT (bipartite):

    ...
    99970,4,6,-70531.4,0,0,0,0,0,1,0,0,0,0,0,0,0,0, ...
    99980,4,6,-70529.3,0,0,1,0,0,0,0,0,0,0,0,0,0,0, ...
    99990,4,6,-70529.3,0,0,1,0,0,0,0,0,0,0,0,0,0,0, ...

The output consists of a series of Monte Carlo samples. By default, the only the last 1000 samples are sent to stdout. The first four columns are Monte Carlo sweep number, number of groups Ka, number of groups Kb and the log-likelihood, the latter accurate to within overall additive and multiplicative constants. The successive columns represent the community labels of each node.

  • OUTPUT (unipartite; --uni mode):

    ...
    99970,10,-61119.1,0,0,0,0,0,1,0,0,0,0,0,0,0,0, ...
    99980,10,-61118.3,0,0,1,0,0,0,0,0,0,0,0,0,0,0, ...
    99990,10,-61120,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0, ...

Similarly, the output consists of a series of Monte Carlo samples and only the last 1000 samples are printed.
The first four columns are Monte Carlo sweep number, number of groups K and the log-likelihood, the latter accurate to within overall additive and multiplicative constants. The successive columns represent the community labels of each node.

Further specs

Cooling schedule

Four cooling schedules are implemented: exponential, linear, logarithmic, constant, and abrupt_cool. The default option is the abrupt cooling one (abrupt_cool), yet it is advised to test these annealing schemes in order to decide which one best approaches the maximum a posteriori state.

There inverse temperature is given as

beta(t) = 1/T_0 * alpha^(-t)                (Exponential)
beta(t) = 1/T_0 * [1 - eta * t / T_0]^(-1)  (Linear)
beta(t) = log(t + d) / c                    (Logarithmic)
beta(t) = 1 / T_0                           (Constant)
beta(t) = 1 if t < T_0 else 0               (Abrupt Cooling)

where t is the MCMC step. The parameters of these cooling schedules are passed like so:

-a T_0 alpha    (Exponential; 1, 0.99)
-a T_0 eta      (Linear; sampling_steps + 1, 1)
-a c d          (Logarithmic; 1, 1)
-a T_0          (Constant; 1)
-a T_0          (Abrupt Cooling; steps_await)

The defaulted parameters are listed in the parentheses. Note that <param_2> is not required when the cooling schedule is constant or abrupt_cool.

Optional membership file

When one wants to initiate a customized configuration, one should prepare a file, say optional_membership_file.txt, which contains one community label per line.

<community_id_of_node_id_1>
<community_id_of_node_id_2>
...
<community_id_of_node_id_n>

Companion article

Please cite:

Estimating the number of communities in a bipartite network

Tzu-Chi Yen and Daniel Larremore, in preparation.

Acknowledgement

Tzu-Chi Yen wishes to thank Jean-Gabriel Young, whose sbm_canonical_mcmc project inspires the code design pattern of this project.

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