COMMUNITY DETECTION_python-louvain

Python-louvain Package

pip install python-louvain

import community

#first compute the best partition
partition = community.best_partition(G)

#Drawing partition

Method 1: 

#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()

Method 2: 

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)

Supplementary knowledge:

1. what is the partition of graphs.

partition: dict; {key (nodes_id): values(community_id)}

2.  function : community.best_partition(G)

Returns
    -------
    partition : dictionnary
       The partition, with communities numbered from 0 to number of communities

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