ó ú£Õ\c@sRddlZdjddgƒZdgZddd„Zddddd„ZdS( iÿÿÿÿNs s%Jordi Torrents sAric Hagberg (hagberg@lanl.gov)taverage_neighbor_degreecs½i}x°||dˆƒjƒD]–\‰}|dkr@d}n|ˆˆƒ}ˆdkrt|jƒƒt|ƒ|ˆs(titemstNonetsumtvaluestfloat(Rt source_degreet target_degreetnodesRtavgtdegtnbrdeg((RRRsi/Users/dxp/prism/prism-games/prism-examples/smgs/car/networkx/algorithms/assortativity/neighbor_degree.pyt_average_nbr_deg s%   #%toutcCsk|j}|j}|jƒrOi|jd6|jd6}||}||}nt|||d|d|ƒS(séReturns the average degree of the neighborhood of each node. The average 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.edge[0][1]['weight'] = 5 >>> G.edge[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() >>> G.add_path([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). RtinRR(tdegreet is_directedt out_degreet in_degreeR(RtsourcettargetRRR Rt direction((si/Users/dxp/prism/prism-games/prism-examples/smgs/car/networkx/algorithms/assortativity/neighbor_degree.pyRsS       (tnetworkxtnxtjoint __author__t__all__R RR(((si/Users/dxp/prism/prism-games/prism-examples/smgs/car/networkx/algorithms/assortativity/neighbor_degree.pyts