def topic_coupling(model, threshold=None, **kwargs):
"""
Two papers are coupled if they both contain a shared topic above a
``threshold``.
Parameters
----------
model : :class:`.LDAModel`
threshold : float
Default: ``3./model.Z``
kwargs : kwargs
Passed on to :func:`.coupling`\.
Returns
-------
:ref:`networkx.Graph <networkx:graph>`
"""
if not threshold:
threshold = 3. / model.Z
select = lambda f, v, c, dc: v > threshold
graph = coupling(model.corpus, 'topics', filter=select, **kwargs)
graph.name = ''
return graph
def test_coupling(self):
g = coupling(self.corpus, 'citations')
self.assertIsInstance(g, nx.Graph)
self.assertGreater(len(g.nodes()), 0)
self.assertGreater(len(g.edges()), 0)
for s, t, attrs in g.edges(data=True):
self.assertEqual(len(attrs['features']), attrs['weight'])
def bibliographic_coupling(corpus, min_weight=1, **kwargs):
"""
Generate a bibliographic coupling network.
Two papers are **bibliographically coupled** when they both cite the same,
third, paper.
"""
return coupling(corpus, 'citations', min_weight=min_weight, **kwargs)
def test_coupling_min_weight(self):
"""
Limit edges to weight >= 3.
"""
min_weight = 3
g = coupling(self.corpus, 'citations')
g2 = coupling(self.corpus, 'citations', min_weight=min_weight)
self.assertIsInstance(g, nx.Graph)
self.assertGreater(len(g2.nodes()), 0)
self.assertGreater(len(g.nodes()), len(g2.nodes()))
self.assertGreater(len(g2.edges()), 0)
self.assertGreater(len(g.edges()), len(g2.edges()))
for s, t, attrs in g2.edges(data=True):
self.assertGreaterEqual(attrs['weight'], min_weight)
def author_coupling(corpus, min_weight=1, **kwargs):
return coupling(corpus, 'authors', min_weight=min_weight, **kwargs)