wehub-resource-sync
58 lines
1.5 KiB
Python
58 lines
1.5 KiB
Python
__all__ = ["ego_betweenness"]
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import numpy as np
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from easygraph.utils import *
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@not_implemented_for("multigraph")
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def ego_betweenness(G, node):
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"""
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ego networks are networks consisting of a single actor (ego) together with the actors they are connected to (alters) and all the links among those alters.[1]
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Burt (1992), in his book Structural Holes, provides ample evidence that having high betweenness centrality, which is highly correlated with having many structural holes, can bring benefits to ego.[1]
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Returns the betweenness centrality of a ego network whose ego is set
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Parameters
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----------
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G : graph
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node : int
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Returns
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-------
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sum : float
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the betweenness centrality of a ego network whose ego is set
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Examples
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--------
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Returns the betwenness centrality of node 1.
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>>> ego_betweenness(G,node=1)
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Reference
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---------
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.. [1] Martin Everett, Stephen P. Borgatti. "Ego network betweenness." Social Networks, Volume 27, Issue 1, Pages 31-38, 2005.
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"""
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g = G.ego_subgraph(node)
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print(g.edges)
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print(g.nodes)
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n = len(g)
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A = np.zeros((n, n))
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for i in range(n):
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for j in range(n):
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if g.has_edge(g.index2node[i], g.index2node[j]):
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A[i, j] = 1
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B = A * A
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C = np.identity(n) - A
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sum = 0
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flag = G.is_directed()
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for i in range(n):
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for j in range(n):
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if i != j and C[i, j] == 1 and B[i, j] != 0:
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sum += 1.0 / B[i, j]
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if flag == False:
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sum /= 2
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return sum
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