annotate env/lib/python3.9/site-packages/networkx/algorithms/assortativity/neighbor_degree.py @ 0:4f3585e2f14b draft default tip

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

__all__ = ["average_neighbor_degree"]


def _average_nbr_deg(G, source_degree, target_degree, nodes=None, weight=None):
    # average degree of neighbors
    avg = {}
    for n, deg in source_degree(nodes, weight=weight):
        # normalize but not by zero degree
        if deg == 0:
            deg = 1
        nbrdeg = target_degree(G[n])
        if weight is None:
            avg[n] = sum(d for n, d in nbrdeg) / float(deg)
        else:
            avg[n] = sum((G[n][nbr].get(weight, 1) * d for nbr, d in nbrdeg)) / float(
                deg
            )
    return avg


def average_neighbor_degree(G, source="out", target="out", nodes=None, weight=None):
    r"""Returns the average degree of the neighborhood of each node.

    The average neighborhood degree of a node `i` is

    .. math::

        k_{nn,i} = \frac{1}{|N(i)|} \sum_{j \in N(i)} k_j

    where `N(i)` are the neighbors of node `i` and `k_j` is
    the degree of node `j` which belongs to `N(i)`. For weighted
    graphs, an analogous measure can be defined [1]_,

    .. math::

        k_{nn,i}^{w} = \frac{1}{s_i} \sum_{j \in N(i)} w_{ij} k_j

    where `s_i` is the weighted degree of node `i`, `w_{ij}`
    is the weight of the edge that links `i` and `j` and
    `N(i)` are the neighbors of node `i`.


    Parameters
    ----------
    G : NetworkX graph

    source : string ("in"|"out")
       Directed graphs only.
       Use "in"- or "out"-degree for source node.

    target : string ("in"|"out")
       Directed graphs only.
       Use "in"- or "out"-degree for target node.

    nodes : list or iterable, optional
        Compute neighbor degree for specified nodes.  The default is
        all nodes in the graph.

    weight : string or None, optional (default=None)
       The edge attribute that holds the numerical value used as a weight.
       If None, then each edge has weight 1.

    Returns
    -------
    d: dict
       A dictionary keyed by node with average neighbors degree value.

    Examples
    --------
    >>> G = nx.path_graph(4)
    >>> G.edges[0, 1]["weight"] = 5
    >>> G.edges[2, 3]["weight"] = 3

    >>> nx.average_neighbor_degree(G)
    {0: 2.0, 1: 1.5, 2: 1.5, 3: 2.0}
    >>> nx.average_neighbor_degree(G, weight="weight")
    {0: 2.0, 1: 1.1666666666666667, 2: 1.25, 3: 2.0}

    >>> G = nx.DiGraph()
    >>> nx.add_path(G, [0, 1, 2, 3])
    >>> nx.average_neighbor_degree(G, source="in", target="in")
    {0: 1.0, 1: 1.0, 2: 1.0, 3: 0.0}

    >>> nx.average_neighbor_degree(G, source="out", target="out")
    {0: 1.0, 1: 1.0, 2: 0.0, 3: 0.0}

    Notes
    -----
    For directed graphs you can also specify in-degree or out-degree
    by passing keyword arguments.

    See Also
    --------
    average_degree_connectivity

    References
    ----------
    .. [1] A. Barrat, M. Barthélemy, R. Pastor-Satorras, and A. Vespignani,
       "The architecture of complex weighted networks".
       PNAS 101 (11): 3747–3752 (2004).
    """
    source_degree = G.degree
    target_degree = G.degree
    if G.is_directed():
        direction = {"out": G.out_degree, "in": G.in_degree}
        source_degree = direction[source]
        target_degree = direction[target]
    return _average_nbr_deg(G, source_degree, target_degree, nodes=nodes, weight=weight)


# obsolete
# def average_neighbor_in_degree(G, nodes=None, weight=None):
#     if not G.is_directed():
#         raise nx.NetworkXError("Not defined for undirected graphs.")
#     return _average_nbr_deg(G, G.in_degree, G.in_degree, nodes, weight)
# average_neighbor_in_degree.__doc__=average_neighbor_degree.__doc__

# def average_neighbor_out_degree(G, nodes=None, weight=None):
#     if not G.is_directed():
#         raise nx.NetworkXError("Not defined for undirected graphs.")
#     return _average_nbr_deg(G, G.out_degree, G.out_degree, nodes, weight)
# average_neighbor_out_degree.__doc__=average_neighbor_degree.__doc__