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 get_cliques_modular(conn, class_mapping, classes_sorted):
degree_frequency = []
max_degree = 0
for clase in classes_sorted:
# select rsn
idx = np.where(class_mapping == clase)[0]
tmp_conn = conn.copy()[np.ix_(idx, idx)]
# convert to Graph
G = nx.from_numpy_array(tmp_conn)
# find cliques
cliques = list(clique.enumerate_all_cliques(G))
clique_degrees = [len(clique) for clique in cliques]
degrees, counts = np.unique(clique_degrees, return_counts=True)
degree_frequency.append(counts)
if np.max(degrees) > max_degree: max_degree = np.max(degrees)
# all frequencies of the same size
ext_degree_frequency = []
for f in degree_frequency:
if len(f) != max_degree:
new_f = np.zeros(max_degree)
new_f[:len(f)] = f
ext_degree_frequency.append(new_f)
else:
ext_degree_frequency.append(f)
degree_names = [f'{k}-clique' for k in range(3, max_degree + 1)]
return ext_degree_frequency, degree_names
def get_cliques_local(conn):
# convert to Graph
G = nx.from_numpy_array(conn)
# find cliques
cliques = list(clique.enumerate_all_cliques(G))
max_degree = len(cliques[-1])
v_counts = []
for degree in range(3, max_degree + 1):
cliques_ = [clique for clique in cliques if len(clique) == degree]
v, counts = np.unique(np.row_stack(cliques_), return_counts=True)
if len(v) != len(conn):
new_counts = np.zeros(len(conn))
new_counts[v] = counts
else:
new_counts = counts
v_counts.append(new_counts)
clique_names = [f'{k}-clique' for k in range(3, max_degree + 1)]
return v_counts, clique_names
def filtered_cliques(graph, condition=None):
if condition is None:
condition = lambda c: len(c) > 1
return [
c for c in enumerate_all_cliques(graph.to_nx())
if graph.parent_node_id not in c and condition(c)
and len(c) < len(graph) - 1
]
def cliques(net):
"""Returns the cliques of a network
Parameters
----------
net : Graph
NetworkX Graph object
Returns
-------
dict
dictionary of cliques of given size
"""
cliques_list = list(clique.enumerate_all_cliques(net))
cliques_dict = dict()
for c in cliques_list:
l = len(c)
try:
cliques_dict[l].append(c)
except KeyError:
cliques_dict[l] = [c]
return cliques_dict
def max_clique(g):
cliques = list(cl.enumerate_all_cliques(g))
print("These are cliques in the graph:")
#one of the biggest
max_clique_graph = g.subgraph(nodes=cliques[-1])
return max_clique_graph
def max_clique(g):
cliques = list(cl.enumerate_all_cliques(g))
max_clique_graph = g.subgraph(nodes=cliques[-1])
return max_clique_graph
data = f.readline()
x = int(str(data.split(' ')[0]))
y = int(str(data.split(' ')[1])) #使用split将字符串以空格符号分开，前一个为x，后一个为y
weight = int(str(data.split(' ')[2]))
cost = int(str(data.split(' ')[3]))
G.add_edge(x, y)
G[x][y]['b'] = weight
G[x][y]['c'] = cost
#G.add_weighted_edges_from([(x,y,weight)]); #在网络中添加x-y这一条边
edgewidth.append(weight * 0.1)
factor = factor + 1
if factor == 96:
break
else:
print 'error'
pos = nx.fruchterman_reingold_layout(G, dim=2, k=0.8, scale=1.5)
#pos=spring_layout(G)
nx.draw_networkx_nodes(G, pos, node_size=100, alpha=0.7)
nx.draw_networkx_labels(G, pos, font_size=9)
edge_labels = nx.get_edge_attributes(G, 'c')
nx.draw_networkx_edges(G, pos)
nx.draw_networkx_edge_labels(G,
pos,
font_size=8,
clip_on=True,
font_color='red')
plt.show()
#聚类
print enumerate_all_cliques(G)
def clique_sizes(graph):
"""clique_sizes"""
return [len(u) for u in list(clique.enumerate_all_cliques(graph)) if len(u) > 1]
def n_cliques(graph):
"""n_cliques"""
return len([u for u in list(clique.enumerate_all_cliques(graph)) if len(u) > 1])
