wg6dZddlZddlmZgdZej dZedej dZedej dZ y) zDegree centrality measures.N)not_implemented_for)degree_centralityin_degree_centralityout_degree_centralityct|dkr|Dcic]}|dc}Sdt|dz z }|jDcic] \}}|||z }}}|Scc}wcc}}w)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. ?)lendegreeGnsd centralitys n/home/mcse/projects/flask/flask-venv/lib/python3.12/site-packages/networkx/algorithms/centrality/degree_alg.pyrr slH 1v{ 1   s1v|A'(xxz2tq!!QU(2J2  !3 AA undirectedct|dkr|Dcic]}|dc}Sdt|dz z }|jDcic] \}}|||z }}}|Scc}wcc}}w)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 )r in_degreer s rrr5slT 1v{ 1   s1v|A'({{}5tq!!QU(5J5  !6rct|dkr|Dcic]}|dc}Sdt|dz z }|jDcic] \}}|||z }}}|Scc}wcc}}w)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 )r out_degreer s rrrgslT 1v{ 1   s1v|A'(||~6tq!!QU(6J6  !7r) __doc__networkxnxnetworkx.utils.decoratorsr__all__ _dispatchablerrrrr!s}!9 P((V\"-#-`\"-#-r