import community

#first compute the best partition

partition = community.best_partition(G)

#drawing

size = float(len(set(partition.values())))

pos = nx.spring_layout(G)

count = 0.

for com in set(partition.values()) :

    count = count + 1.

    list_nodes = [nodes for nodes in partition.keys()

                                if partition[nodes] == com]

    nx.draw_networkx_nodes(G, pos, list_nodes, node_size = 20,

                                node_color = str(count / size))

nx.draw_networkx_edges(G, pos, alpha=0.5)

plt.show()

pos = nx.spring_layout(G)

values = [partition.get(node) for node in G.nodes()]

nx.draw_networkx(G, pos, cmap=plt.get_cmap('magma'), node_color=values, node_size=50, with_labels=False)

def best_partition(graph,

                   partition=None,

                   weight='weight',

                   resolution=1.,

                   randomize=None,

                   random_state=None):

    """Compute the partition of the graph nodes which maximises the modularity

    (or try..) using the Louvain heuristices

    This is the partition of highest modularity, i.e. the highest partition

    of the dendrogram generated by the Louvain algorithm.

    Parameters

    ----------

    graph : networkx.Graph

       the networkx graph which is decomposed

    partition : dict, optional

       the algorithm will start using this partition of the nodes.

       It's a dictionary where keys are their nodes and values the communities

    weight : str, optional

        the key in graph to use as weight. Default to 'weight'

    resolution :  double, optional

        Will change the size of the communities, default to 1.

        represents the time described in

        "Laplacian Dynamics and Multiscale Modular Structure in Networks",

        R. Lambiotte, J.-C. Delvenne, M. Barahona

    randomize : boolean, optional

        Will randomize the node evaluation order and the community evaluation

        order to get different partitions at each call

    random_state : int, RandomState instance or None, optional (default=None)

        If int, random_state is the seed used by the random number generator;

        If RandomState instance, random_state is the random number generator;

        If None, the random number generator is the RandomState instance used

        by `np.random`.

    Returns

    -------

    partition : dictionnary

       The partition, with communities numbered from 0 to number of communities