fi<dZddlZddlmZgdZejdZedejdZedejdZ dS) zDegree centrality measures.N)not_implemented_for)degree_centralityin_degree_centralityout_degree_centralityct|dkrt|dSdt|dz z fd|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. ?c"i|] \}}||z Sr .0ndss /var/www/development/aibuddy-work/election-extract/venv/lib/python3.11/site-packages/networkx/algorithms/centrality/degree_alg.py z%degree_centrality..1s#222tq!!QU222)lendictfromkeysdegreeG centralityrs @rrr sdH 1vv{{}}Q""" s1vv|A2222qxxzz222J r undirectedct|dkrt|dSdt|dz z fd|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. rr c"i|] \}}||z Sr r r s rrz(in_degree_centrality..cs#555tq!!QU555r)rrr in_degreers @rrr5sdT 1vv{{}}Q""" s1vv|A5555q{{}}555J rct|dkrt|dSdt|dz z fd|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. rr c"i|] \}}||z Sr r r s rrz)out_degree_centrality..s#666tq!!QU666r)rrr out_degreers @rrrgsdT 1vv{{}}Q""" s1vv|A6666q||~~666J r) __doc__networkxnxnetworkx.utils.decoratorsr__all__ _dispatchablerrrr rrr(s!!999999 P P P(((V\""--#"-`\""--#"---r