+ jRt^RIt^RIHt.ROt]P R4t]!R4]P R44t]!R4]P R44t R#)zDegree centrality measures.N)not_implemented_forc\V4^8:dVUu/uFq^bK up#R\V4R, , pVP4UUu/uFwrWV,bK pppV#uupiuuppi)aCompute the degree centrality for nodes. The degree centrality for a node v is the fraction of nodes it is connected to. For directed graphs it is the average number of in and out edges per node. 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} For directed graphs, values are in [0, 2] and use the sum of in-degree and out-degree. Consider `in_degree_centrality` and `out_degree_centrality` for directed graphs too. >>> D = nx.DiGraph([(0, 1), (0, 2), (1, 2), (2, 0)]) >>> nx.degree_centrality(D) {0: 1.5, 1: 1.0, 2: 1.5} See Also -------- betweenness_centrality load_centrality eigenvector_centrality in_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. ?)lendegreeGnsd centralitys& j/home/luca/screen-checking/.venv/lib/python3.14/site-packages/networkx/algorithms/centrality/degree_alg.pydegree_centralityr skd 1v{ a1a   s1v|A'(xxz2ztq!U(zJ2  !3 A(A- undirectedc\V4^8:dVUu/uFq^bK up#R\V4R, , pVP4UUu/uFwrWV,bK pppV#uupiuuppi)aCompute 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. r)r in_degreers& r in_degree_centralityrCskT 1v{ a1a   s1v|A'({{}5}tq!U(}J5  !6rc\V4^8:dVUu/uFq^bK up#R\V4R, , pVP4UUu/uFwrWV,bK pppV#uupiuuppi)awCompute 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. r)r out_degreers& r out_degree_centralityruskT 1v{ a1a   s1v|A'(||~6~tq!U(~J6  !7r)rrr) __doc__networkxnxnetworkx.utils.decoratorsr__all__ _dispatchablerrrr rs}!9 P66r\"-#-`\"-#-r