Source code for networkx.algorithms.centrality.degree_alg

"""Degree centrality measures."""
import networkx as nx
from networkx.utils.decorators import not_implemented_for

__all__ = ["degree_centrality", "in_degree_centrality", "out_degree_centrality"]


[docs] @nx._dispatch def degree_centrality(G): """Compute 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. """ if len(G) <= 1: return {n: 1 for n in G} s = 1.0 / (len(G) - 1.0) centrality = {n: d * s for n, d in G.degree()} return centrality
[docs] @not_implemented_for("undirected") @nx._dispatch def in_degree_centrality(G): """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. """ if len(G) <= 1: return {n: 1 for n in G} s = 1.0 / (len(G) - 1.0) centrality = {n: d * s for n, d in G.in_degree()} return centrality
[docs] @not_implemented_for("undirected") @nx._dispatch def out_degree_centrality(G): """Compute 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. """ if len(G) <= 1: return {n: 1 for n in G} s = 1.0 / (len(G) - 1.0) centrality = {n: d * s for n, d in G.out_degree()} return centrality