Python networkx.adamic_adar_index() Examples
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code examples of networkx.adamic_adar_index().
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Example #1
| Source File: aa.py From GEM-Benchmark with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def get_edge_weight(self, i, j):
aa_index = nx.adamic_adar_index(self._G, [(i, j)])
return six.next(aa_index)[2]
Example #2
| Source File: link_prediction_utils.py From gcnn-survey-paper with Apache License 2.0 | 5 votes |
|
def adamic_adar(self):
"""Computes adamic adar scores."""
graph = nx.from_scipy_sparse_matrix(self.adj_matrix)
scores = nx.adamic_adar_index(graph)
return scores
Example #3
| Source File: test_link_prediction.py From qgisSpaceSyntaxToolkit with GNU General Public License v3.0 | 5 votes |
|
def setUp(self):
self.func = nx.adamic_adar_index
self.test = partial(_test_func, predict_func=self.func)
Example #4
| Source File: test_link_prediction.py From Carnets with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def setUp(self):
self.func = nx.adamic_adar_index
self.test = partial(_test_func, predict_func=self.func)
Example #5
| Source File: test_link_prediction.py From aws-kube-codesuite with Apache License 2.0 | 5 votes |
|
def setUp(self):
self.func = nx.adamic_adar_index
self.test = partial(_test_func, predict_func=self.func)
Example #6
| Source File: link_prediction.py From qgisSpaceSyntaxToolkit with GNU General Public License v3.0 | 4 votes |
|
def adamic_adar_index(G, ebunch=None):
r"""Compute the Adamic-Adar index of all node pairs in ebunch.
Adamic-Adar index of `u` and `v` is defined as
.. math::
\sum_{w \in \Gamma(u) \cap \Gamma(v)} \frac{1}{\log |\Gamma(w)|}
where :math:`\Gamma(u)` denotes the set of neighbors of `u`.
Parameters
----------
G : graph
NetworkX undirected graph.
ebunch : iterable of node pairs, optional (default = None)
Adamic-Adar index will be computed for each pair of nodes given
in the iterable. The pairs must be given as 2-tuples (u, v)
where u and v are nodes in the graph. If ebunch is None then all
non-existent edges in the graph will be used.
Default value: None.
Returns
-------
piter : iterator
An iterator of 3-tuples in the form (u, v, p) where (u, v) is a
pair of nodes and p is their Adamic-Adar index.
Examples
--------
>>> import networkx as nx
>>> G = nx.complete_graph(5)
>>> preds = nx.adamic_adar_index(G, [(0, 1), (2, 3)])
>>> for u, v, p in preds:
... '(%d, %d) -> %.8f' % (u, v, p)
...
'(0, 1) -> 2.16404256'
'(2, 3) -> 2.16404256'
References
----------
.. [1] D. Liben-Nowell, J. Kleinberg.
The Link Prediction Problem for Social Networks (2004).
http://www.cs.cornell.edu/home/kleinber/link-pred.pdf
"""
if ebunch is None:
ebunch = nx.non_edges(G)
def predict(u, v):
return sum(1 / math.log(G.degree(w))
for w in nx.common_neighbors(G, u, v))
return ((u, v, predict(u, v)) for u, v in ebunch)
Example #7
| Source File: link_prediction.py From Carnets with BSD 3-Clause "New" or "Revised" License | 4 votes |
|
def adamic_adar_index(G, ebunch=None):
r"""Compute the Adamic-Adar index of all node pairs in ebunch.
Adamic-Adar index of `u` and `v` is defined as
.. math::
\sum_{w \in \Gamma(u) \cap \Gamma(v)} \frac{1}{\log |\Gamma(w)|}
where $\Gamma(u)$ denotes the set of neighbors of $u$.
Parameters
----------
G : graph
NetworkX undirected graph.
ebunch : iterable of node pairs, optional (default = None)
Adamic-Adar index will be computed for each pair of nodes given
in the iterable. The pairs must be given as 2-tuples (u, v)
where u and v are nodes in the graph. If ebunch is None then all
non-existent edges in the graph will be used.
Default value: None.
Returns
-------
piter : iterator
An iterator of 3-tuples in the form (u, v, p) where (u, v) is a
pair of nodes and p is their Adamic-Adar index.
Examples
--------
>>> import networkx as nx
>>> G = nx.complete_graph(5)
>>> preds = nx.adamic_adar_index(G, [(0, 1), (2, 3)])
>>> for u, v, p in preds:
... '(%d, %d) -> %.8f' % (u, v, p)
...
'(0, 1) -> 2.16404256'
'(2, 3) -> 2.16404256'
References
----------
.. [1] D. Liben-Nowell, J. Kleinberg.
The Link Prediction Problem for Social Networks (2004).
http://www.cs.cornell.edu/home/kleinber/link-pred.pdf
"""
def predict(u, v):
return sum(1 / log(G.degree(w)) for w in nx.common_neighbors(G, u, v))
return _apply_prediction(G, predict, ebunch)
Example #8
| Source File: link_prediction.py From aws-kube-codesuite with Apache License 2.0 | 4 votes |
|
def adamic_adar_index(G, ebunch=None):
r"""Compute the Adamic-Adar index of all node pairs in ebunch.
Adamic-Adar index of `u` and `v` is defined as
.. math::
\sum_{w \in \Gamma(u) \cap \Gamma(v)} \frac{1}{\log |\Gamma(w)|}
where $\Gamma(u)$ denotes the set of neighbors of $u$.
Parameters
----------
G : graph
NetworkX undirected graph.
ebunch : iterable of node pairs, optional (default = None)
Adamic-Adar index will be computed for each pair of nodes given
in the iterable. The pairs must be given as 2-tuples (u, v)
where u and v are nodes in the graph. If ebunch is None then all
non-existent edges in the graph will be used.
Default value: None.
Returns
-------
piter : iterator
An iterator of 3-tuples in the form (u, v, p) where (u, v) is a
pair of nodes and p is their Adamic-Adar index.
Examples
--------
>>> import networkx as nx
>>> G = nx.complete_graph(5)
>>> preds = nx.adamic_adar_index(G, [(0, 1), (2, 3)])
>>> for u, v, p in preds:
... '(%d, %d) -> %.8f' % (u, v, p)
...
'(0, 1) -> 2.16404256'
'(2, 3) -> 2.16404256'
References
----------
.. [1] D. Liben-Nowell, J. Kleinberg.
The Link Prediction Problem for Social Networks (2004).
http://www.cs.cornell.edu/home/kleinber/link-pred.pdf
"""
def predict(u, v):
return sum(1 / log(G.degree(w)) for w in nx.common_neighbors(G, u, v))
return _apply_prediction(G, predict, ebunch)
