community_louvain.py 文件源码

def induced_graph(partition, graph, weight="weight"):
    """Produce the graph where nodes are the communities

    there is a link of weight w between communities if the sum of the weights
    of the links between their elements is w

    Parameters
    ----------
    partition : dict
       a dictionary where keys are graph nodes and  values the part the node
       belongs to
    graph : networkx.Graph
        the initial graph
    weight : str, optional
        the key in graph to use as weight. Default to 'weight'


    Returns
    -------
    g : networkx.Graph
       a networkx graph where nodes are the parts

    Examples
    --------
    >>> n = 5
    >>> g = nx.complete_graph(2*n)
    >>> part = dict([])
    >>> for node in g.nodes() :
    >>>     part[node] = node % 2
    >>> ind = induced_graph(part, g)
    >>> goal = nx.Graph()
    >>> goal.add_weighted_edges_from([(0,1,n*n),(0,0,n*(n-1)/2), (1, 1, n*(n-1)/2)])  # NOQA
    >>> nx.is_isomorphic(int, goal)
    True
    """
    ret = nx.Graph()
    ret.add_nodes_from(partition.values())

    for node1, node2, datas in graph.edges_iter(data=True):
        edge_weight = datas.get(weight, 1)
        com1 = partition[node1]
        com2 = partition[node2]
        w_prec = ret.get_edge_data(com1, com2, {weight: 0}).get(weight, 1)
        ret.add_edge(com1, com2, attr_dict={weight: w_prec + edge_weight})

    return ret