def CliqueNumEqualsChromaticNum(G):
result = False
if (graph_clique_number(G) == chromatic_number(G)):
result = True
return result
def ChromaticNumberEqualsCLiqueNumber(G):
if(chromatic_number(G) == graph_clique_number(G)):
result = True
else:
result = False
return result
def compute_features(self):
# graph clique number
self.add_feature(
"graph_clique_number",
lambda graph: clique.graph_clique_number(graph),
"The clique number of a graph is the size of the largest clique in the graph",
InterpretabilityScore(3),
)
# number of maximal cliques
self.add_feature(
"num_max_cliques",
lambda graph: clique.graph_number_of_cliques(graph),
"The number of maximal cliques in the graph",
InterpretabilityScore(3),
)
n_cliques = lambda graph: len([
u for u in list(clique.enumerate_all_cliques(graph)) if len(u) > 1
])
self.add_feature(
"num_cliques",
n_cliques,
"The number of cliques in the graph",
InterpretabilityScore(3),
)
clique_sizes = lambda graph: [
len(u) for u in list(clique.enumerate_all_cliques(graph))
if len(u) > 1
]
self.add_feature(
"clique_sizes",
clique_sizes,
"the distribution of clique sizes",
InterpretabilityScore(3),
statistics="centrality",
)
@lru_cache(maxsize=None)
def eval_cliques(graph):
"""this evaluates the main function and cach it for speed up."""
cliques = [
len(u) for u in list(clique.find_cliques(graph)) if len(u) > 1
]
return np.bincount(cliques)[np.nonzero(np.bincount(cliques))]
maximal_clique_sizes = (
lambda graph: eval_cliques(graph)[0] / eval_cliques(graph)[-1])
self.add_feature(
"clique_sizes_maximal",
maximal_clique_sizes,
"the ratio of number of max and min size cliques",
InterpretabilityScore(3),
)
def sizeoflargestclique(a):
from networkx.algorithms import approximation, clique
largestcliquesize = clique.graph_clique_number(a)
print(largestcliquesize)
#add the new node
vertexToAddNum = resultGraph.number_of_nodes()
resultGraph.add_node(vertexToAddNum)
x_sets[vertexIndex % CYCLE_LENGTH].append(vertexToAddNum)
#add its edges
resultGraph.add_edge( (vertexToAddNum - 1) % CYCLE_LENGTH, vertexToAddNum )
resultGraph.add_edge( vertexToAddNum % CYCLE_LENGTH, vertexToAddNum)
resultGraph.add_edge( (vertexToAddNum + 1) % CYCLE_LENGTH, vertexToAddNum)
#x_i forms a clique
for thisXSet in x_sets:
if(len(thisXSet) > 1):
for thisCliqueEdge in product(thisXSet, thisXSet):
if(thisCliqueEdge[0] != thisCliqueEdge[1]):
resultGraph.add_edge(thisCliqueEdge[0], thisCliqueEdge[1])
#X_i joins its neighbours
for thisXSetIndex in range(0,CYCLE_LENGTH):
x1 = x_sets[thisXSetIndex]
x2 = x_sets[(thisXSetIndex+1) % CYCLE_LENGTH]
for vertexI in x1:
for vertexJ in x2:
resultGraph.add_edge(vertexI,vertexJ)
return resultGraph
result = Construct()
print("Clique number: {0}".format(graph_clique_number(result)))
f = File(DIRECTORY, G = result, logger = MY_LOGGER, base="C5-")
f.save()
