Generating networks
===================

Generating networks in CiGRAM is straightforward.

.. code-block:: python

    from cigram import generate_graph
    n = 1000
    avg_k = 4.95
    k = 1
    graph, positions, communities = cigram_graph(n, avg_k, k)

Here n is the number of nodes, avg_k is the desired average degree and k is the number of communities.
Note that the number of communities here is fixed at 1, so the graph will not generate artificial clusters.

The resulting returned tuple of ``graph``, ``positions`` and ``communities`` is a networkx graph object, a dictionary
of points upon a unit circle for each node, and a dictionary for the community membership of each vertex.

More complex parameters allow you to generate different heterogenous degree distributions.
This is controlled by the parameters sigma_f and sigma_r which have an effect on the underlying probability space for
the connections between nodes.


.. code-block:: python

    sigma_r = 0.8
    sigma_f = 0.8
    graph, positions, communities = cigram_graph(n, avg_k, k)


To generate networks with assortativity this can be specified with the parameter a (by default this is 0).

LFR Benchmarks
==============
CiGRAM also includes the generation of Lancichinetti–Fortunato–Radicchi (LFR) benchamrks.
This was implemented due to issues found with the python implementation in NetworkX.
This version is mostly a tidied up version of the original C++ code.
To use:

.. code-block:: python

    from cigram import lfr_benchmark_graph

    params = {
        'n': 10000,
        'average_degree': 10,
        'max_degree': 1000,
        'mu': 0.5,
        'tau': 2.0,
        'tau2': 2.0,
        'minc_size': 3,
        'maxc_size': 1000,
        'overlapping_nodes': 0,
        'overlapping_memberships': 1,
        'seed': 1337
    }

    graph, comms = lfr_benchmark_graph(**params)