U ?h)@s`dZddlZddlmZdddgZejddZejedd dZejedd dZ dS) zDegree centrality measures.N)not_implemented_fordegree_centralityin_degree_centralityout_degree_centralitycsDt|dkrdd|DSdt|dfdd|D}|S)aCompute the degree centrality for nodes. The degree centrality for a node v is the fraction of nodes it is connected to. Parameters ---------- G : graph A networkx graph Returns ------- nodes : dictionary Dictionary of nodes with degree centrality as the value. Examples -------- >>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)]) >>> nx.degree_centrality(G) {0: 1.0, 1: 1.0, 2: 0.6666666666666666, 3: 0.6666666666666666} See Also -------- betweenness_centrality, load_centrality, eigenvector_centrality Notes ----- The degree centrality values are normalized by dividing by the maximum possible degree in a simple graph n-1 where n is the number of nodes in G. For multigraphs or graphs with self loops the maximum degree might be higher than n-1 and values of degree centrality greater than 1 are possible. cSsi|] }|dqSr.0nrr[/var/www/html/venv/lib/python3.8/site-packages/networkx/algorithms/centrality/degree_alg.py -sz%degree_centrality..?csi|]\}}||qSrrr r dsrr r 0s)lenZdegreeGZ centralityrrr rs $ Z undirectedcsDt|dkrdd|DSdt|dfdd|D}|S)a Compute the in-degree centrality for nodes. The in-degree centrality for a node v is the fraction of nodes its incoming edges are connected to. Parameters ---------- G : graph A NetworkX graph Returns ------- nodes : dictionary Dictionary of nodes with in-degree centrality as values. Raises ------ NetworkXNotImplemented If G is undirected. Examples -------- >>> G = nx.DiGraph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)]) >>> nx.in_degree_centrality(G) {0: 0.0, 1: 0.3333333333333333, 2: 0.6666666666666666, 3: 0.6666666666666666} See Also -------- degree_centrality, out_degree_centrality Notes ----- The degree centrality values are normalized by dividing by the maximum possible degree in a simple graph n-1 where n is the number of nodes in G. For multigraphs or graphs with self loops the maximum degree might be higher than n-1 and values of degree centrality greater than 1 are possible. rcSsi|] }|dqSrrr rrr r _sz(in_degree_centrality..rcsi|]\}}||qSrrrrrr r bs)rZ in_degreerrrr r4s * csDt|dkrdd|DSdt|dfdd|D}|S)aCompute the out-degree centrality for nodes. The out-degree centrality for a node v is the fraction of nodes its outgoing edges are connected to. Parameters ---------- G : graph A NetworkX graph Returns ------- nodes : dictionary Dictionary of nodes with out-degree centrality as values. Raises ------ NetworkXNotImplemented If G is undirected. Examples -------- >>> G = nx.DiGraph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)]) >>> nx.out_degree_centrality(G) {0: 1.0, 1: 0.6666666666666666, 2: 0.0, 3: 0.0} See Also -------- degree_centrality, in_degree_centrality Notes ----- The degree centrality values are normalized by dividing by the maximum possible degree in a simple graph n-1 where n is the number of nodes in G. For multigraphs or graphs with self loops the maximum degree might be higher than n-1 and values of degree centrality greater than 1 are possible. rcSsi|] }|dqSrrr rrr r sz)out_degree_centrality..rcsi|]\}}||qSrrrrrr r s)rZ out_degreerrrr rfs * ) __doc__ZnetworkxnxZnetworkx.utils.decoratorsr__all__Z _dispatchrrrrrrr s   + 0