"""
Communicability.
"""
import networkx as nx
from networkx.utils import not_implemented_for
__all__ = ["communicability", "communicability_exp"]
[docs]@not_implemented_for("directed")
@not_implemented_for("multigraph")
def communicability(G):
r"""Returns communicability between all pairs of nodes in G.
The communicability between pairs of nodes in G is the sum of
walks of different lengths starting at node u and ending at node v.
Parameters
----------
G: graph
Returns
-------
comm: dictionary of dictionaries
Dictionary of dictionaries keyed by nodes with communicability
as the value.
Raises
------
NetworkXError
If the graph is not undirected and simple.
See Also
--------
communicability_exp:
Communicability between all pairs of nodes in G using spectral
decomposition.
communicability_betweenness_centrality:
Communicability betweeness centrality for each node in G.
Notes
-----
This algorithm uses a spectral decomposition of the adjacency matrix.
Let G=(V,E) be a simple undirected graph. Using the connection between
the powers of the adjacency matrix and the number of walks in the graph,
the communicability between nodes `u` and `v` based on the graph spectrum
is [1]_
.. math::
C(u,v)=\sum_{j=1}^{n}\phi_{j}(u)\phi_{j}(v)e^{\lambda_{j}},
where `\phi_{j}(u)` is the `u\rm{th}` element of the `j\rm{th}` orthonormal
eigenvector of the adjacency matrix associated with the eigenvalue
`\lambda_{j}`.
References
----------
.. [1] Ernesto Estrada, Naomichi Hatano,
"Communicability in complex networks",
Phys. Rev. E 77, 036111 (2008).
https://arxiv.org/abs/0707.0756
Examples
--------
>>> G = nx.Graph([(0, 1), (1, 2), (1, 5), (5, 4), (2, 4), (2, 3), (4, 3), (3, 6)])
>>> c = nx.communicability(G)
"""
import numpy
nodelist = list(G) # ordering of nodes in matrix
A = nx.to_numpy_array(G, nodelist)
# convert to 0-1 matrix
A[A != 0.0] = 1
w, vec = numpy.linalg.eigh(A)
expw = numpy.exp(w)
mapping = dict(zip(nodelist, range(len(nodelist))))
c = {}
# computing communicabilities
for u in G:
c[u] = {}
for v in G:
s = 0
p = mapping[u]
q = mapping[v]
for j in range(len(nodelist)):
s += vec[:, j][p] * vec[:, j][q] * expw[j]
c[u][v] = float(s)
return c
[docs]@not_implemented_for("directed")
@not_implemented_for("multigraph")
def communicability_exp(G):
r"""Returns communicability between all pairs of nodes in G.
Communicability between pair of node (u,v) of node in G is the sum of
walks of different lengths starting at node u and ending at node v.
Parameters
----------
G: graph
Returns
-------
comm: dictionary of dictionaries
Dictionary of dictionaries keyed by nodes with communicability
as the value.
Raises
------
NetworkXError
If the graph is not undirected and simple.
See Also
--------
communicability:
Communicability between pairs of nodes in G.
communicability_betweenness_centrality:
Communicability betweeness centrality for each node in G.
Notes
-----
This algorithm uses matrix exponentiation of the adjacency matrix.
Let G=(V,E) be a simple undirected graph. Using the connection between
the powers of the adjacency matrix and the number of walks in the graph,
the communicability between nodes u and v is [1]_,
.. math::
C(u,v) = (e^A)_{uv},
where `A` is the adjacency matrix of G.
References
----------
.. [1] Ernesto Estrada, Naomichi Hatano,
"Communicability in complex networks",
Phys. Rev. E 77, 036111 (2008).
https://arxiv.org/abs/0707.0756
Examples
--------
>>> G = nx.Graph([(0, 1), (1, 2), (1, 5), (5, 4), (2, 4), (2, 3), (4, 3), (3, 6)])
>>> c = nx.communicability_exp(G)
"""
import scipy.linalg
nodelist = list(G) # ordering of nodes in matrix
A = nx.to_numpy_array(G, nodelist)
# convert to 0-1 matrix
A[A != 0.0] = 1
# communicability matrix
expA = scipy.linalg.expm(A)
mapping = dict(zip(nodelist, range(len(nodelist))))
c = {}
for u in G:
c[u] = {}
for v in G:
c[u][v] = float(expA[mapping[u], mapping[v]])
return c