def generate_graph_by_gt(after=None, before=None):
submissions = [
s for s in Submission.objects.filter(
id__in=[comment.submission_id for comment in Comment.objects.all()]).prefetch_related('author')]
graph = gt.Graph()
graph.vp['author'] = graph.new_vp('string')
for submission in submissions:
authors = set()
if submission.created_at.replace(tzinfo=None) >= after and submission.created_at.replace(tzinfo=None) <= before:
authors.add(submission.author.name)
authors = authors | set(
comment.author.name for comment in Comment.objects.filter(
submission=submission, created_at__gte=after, created_at__lte=before).prefetch_related('author'))
s_graph = complete_graph(len(authors))
s_graph.vertex_properties['author'] = s_graph.new_vp('string')
s_graph.vertex_properties['author_idx'] = s_graph.new_vp('int')
authors = list(authors)
for author_idx, v in enumerate(s_graph.get_vertices()):
s_graph.vertex_properties['author'][v] = authors[author_idx]
rv = find_vertex(graph, graph.vp['author'], s_graph.vp['author'][v])
s_graph.vertex_properties['author_idx'][v] = graph.vertex_index[rv[0]] if rv else -1
graph, props = graph_union(graph, s_graph, s_graph.vp['author_idx'], [(graph.vp['author'], s_graph.vp['author'])])
graph.vp['author'] = props[0]
return graph
def add_in_graph(gInit, n, graphGen, *args):
'''
Add into initial input graph -gInit-, -n- disconnected graphs that are produced by the graphGen(*args) function.
Input:
-gInit- initial graph. If equals to None, then the output Graph will be comprised by -n- disconnected components each produced by graphGen(*args).
-n- the number of graphs that we will add to gInit.
-graphGen- a function that is producing a specific type of graph, e.g. create_star_graph
-*args- the arguments with witch graphGen will be called.
Example:
Calling add_in_graph(None, 10, generation.complete_graph, 5)
produces a graph that is comprised by 10 complete graphs of size 5 each.
'''
if n <= 1 and gInit != None:
return generation.graph_union(graphGen(*args), gInit)
elif n <= 1 and gInit == None:
return graphGen(*args)
else:
return generation.graph_union(
graphGen(*args), add_in_graph(gInit, n - 1, graphGen, *args))
def append_lattices(g, nLatte=20, latticeSize=[5, 5]):
def isCorner(gLatte, node):
if gLatte.vertex(node).out_degree() == 2: return True
else: return False
gLatte = add_in_graph(None, nLatte, generation.lattice, latticeSize)
gLatte.vp["late"] = gLatte.new_vertex_property("short")
gLatte.vp["late"].a = np.ones(gLatte.num_vertices())
gFinal = generation.graph_union(g, gLatte, internal_props=True)
comp, hist = topology.label_components(gFinal)
numOfCC = max(comp.a)
assert (numOfCC == nLatte)
bbNodes = set(np.where(gFinal.vp["bb"].a == 1)[0])
attached = set(np.where(gFinal.vp["attachments"].a == 1)[0])
freeBBnodes = bbNodes - attached
if freeBBnodes < nLatte:
warnings.warn(
"Not enough free nodes in the erdos graph to attach all the lattices."
)
return None
freeBBnodes = list(freeBBnodes)
np.random.shuffle(freeBBnodes)
k = 0
for cc in range(1, numOfCC + 1):
atNode = np.where(comp.a == cc)[0][0]
assert (isCorner(gFinal, atNode)
) # The first node of a generation.lattice graph is a corner.
gFinal.add_edge(atNode, freeBBnodes[k])
k += 1
gFinal.vp["attachments"].a[atNode] = 1
gFinal.vp["attachments"].a[freeBBnodes[k]] = 1
assert (topology.label_components(gFinal)[1][0] == gFinal.num_vertices()
) # gFinal must be Fully Connected.
return gFinal
def merge_graphs(self, g1, g2):
self._g = graph_union(g1._g, g2._g, internal_props=True)
def erdos_circles_cliques(bbSize,
nCircles,
circleSize,
nCliques,
cliqueSize,
save=True):
'''
Makes a random (Erdos-Renyi) graph connected with circles and cliques.
Input:
-bbSize[0]- how many nodes the random Graph will have
-bbSize[1]- how many edges the random Graph will have
-nCliques- how many cliques to add of size -cliqueSize-
-nCircles- how many circles to add of size -circleSize-
'''
backbone = erdos_renyi_graph(bbSize[0],
bbSize[1],
directed=False,
gcc=True)
print "The random backbone graph has %d vertices and %d edges." % (
backbone.num_vertices(), backbone.num_edges())
assert (np.sum(topology.label_largest_component(backbone).a) ==
backbone.num_vertices())
if backbone.num_vertices() < nCircles + nCliques:
warnings.warn(
"The erdos part of the graph is too small to add the requested circles/cliques."
)
backbone.vp["bb"] = backbone.new_vertex_property(
"short") # Mark all nodes belonging in the random backbone.
backbone.vp["bb"].a = np.ones(backbone.num_vertices())
gCircle = add_in_graph(None, nCircles, generation.circular_graph,
circleSize)
gCircle.vp["circ"] = gCircle.new_vertex_property(
"short") # Mark all nodes belonging in the Circles.
gCircle.vp["circ"].a = np.ones(gCircle.num_vertices())
gCliq = add_in_graph(None, nCliques, generation.complete_graph, cliqueSize)
gCliq.vp["cliq"] = gCliq.new_vertex_property(
"short") # Mark all nodes belonging in the Cliques.
gCliq.vp["cliq"].a = np.ones(gCliq.num_vertices())
concat1 = generation.graph_union(backbone, gCliq, internal_props=True)
gFinal = generation.graph_union(concat1, gCircle, internal_props=True)
assert (sum(gFinal.vp['cliq'].a == 1) == gCliq.num_vertices() and \
sum(gFinal.vp['circ'].a == 1) == gCircle.num_vertices())
comp, hist = topology.label_components(
gFinal) # Mark to which CC every node is.
numOfCC = max(comp.a)
assert (numOfCC == nCircles + nCliques)
bbNodes = np.where(gFinal.vp["bb"].a == 1)[0]
np.random.shuffle(bbNodes)
k = 0
gFinal.vp["attachments"] = gFinal.new_vertex_property(
"short"
) # Bookkeeping which nodes of the backbone where used to connect with the circles/cliques.
for cc in range(1, numOfCC + 1):
atNode = np.where(comp.a == cc)[0][
0] # Since all nodes of the added graphs are equivalent we can pick the 1st to make the attachment.
gFinal.add_edge(atNode, bbNodes[k])
k += 1
gFinal.vp["attachments"].a[atNode] = 1
gFinal.vp["attachments"].a[bbNodes[k]] = 1
assert (topology.label_components(gFinal)[1][0] == gFinal.num_vertices()
) # gFinal must be Fully Connected
print "The graph with the cliques and circles has in total %d vertices and %d edges." % (
gFinal.num_vertices(), gFinal.num_edges())
return gFinal