Source code for networkx.algorithms.assortativity.neighbor_degree

__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


[docs]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__