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Source code for networkx.algorithms.centrality.closeness

Closeness centrality measures.
#    Copyright (C) 2004-2015 by
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    All rights reserved.
#    BSD license.
import functools
import networkx as nx
__author__ = "\n".join(['Aric Hagberg <aric.hagberg@gmail.com>',
                        'Pieter Swart (swart@lanl.gov)',
                        'Sasha Gutfraind (ag362@cornell.edu)'])
__all__ = ['closeness_centrality']

[docs]def closeness_centrality(G, u=None, distance=None, normalized=True): r"""Compute closeness centrality for nodes. Closeness centrality [1]_ of a node `u` is the reciprocal of the sum of the shortest path distances from `u` to all `n-1` other nodes. Since the sum of distances depends on the number of nodes in the graph, closeness is normalized by the sum of minimum possible distances `n-1`. .. math:: C(u) = \frac{n - 1}{\sum_{v=1}^{n-1} d(v, u)}, where `d(v, u)` is the shortest-path distance between `v` and `u`, and `n` is the number of nodes in the graph. Notice that higher values of closeness indicate higher centrality. Parameters ---------- G : graph A NetworkX graph u : node, optional Return only the value for node u distance : edge attribute key, optional (default=None) Use the specified edge attribute as the edge distance in shortest path calculations normalized : bool, optional If True (default) normalize by the number of nodes in the connected part of the graph. Returns ------- nodes : dictionary Dictionary of nodes with closeness centrality as the value. See Also -------- betweenness_centrality, load_centrality, eigenvector_centrality, degree_centrality Notes ----- The closeness centrality is normalized to `(n-1)/(|G|-1)` where `n` is the number of nodes in the connected part of graph containing the node. If the graph is not completely connected, this algorithm computes the closeness centrality for each connected part separately. If the 'distance' keyword is set to an edge attribute key then the shortest-path length will be computed using Dijkstra's algorithm with that edge attribute as the edge weight. References ---------- .. [1] Linton C. Freeman: Centrality in networks: I. Conceptual clarification. Social Networks 1:215-239, 1979. http://leonidzhukov.ru/hse/2013/socialnetworks/papers/freeman79-centrality.pdf """ if distance is not None: # use Dijkstra's algorithm with specified attribute as edge weight path_length = functools.partial(nx.single_source_dijkstra_path_length, weight=distance) else: path_length = nx.single_source_shortest_path_length if u is None: nodes = G.nodes() else: nodes = [u] closeness_centrality = {} for n in nodes: sp = path_length(G,n) totsp = sum(sp.values()) if totsp > 0.0 and len(G) > 1: closeness_centrality[n] = (len(sp)-1.0) / totsp # normalize to number of nodes-1 in connected part if normalized: s = (len(sp)-1.0) / ( len(G) - 1 ) closeness_centrality[n] *= s else: closeness_centrality[n] = 0.0 if u is not None: return closeness_centrality[u] else: return closeness_centrality