def clique_perfects(G, seed_i): G_nodes = G.nodes() S,H = p.convert_graph_connectivity_to_sparse(G, G_nodes) bestQ = [] for itr in xrange(PERFECT_MAXITR): alpha = uniform(.1, .9) Q = map(lambda x: G_nodes[x], p.construct(S, H, alpha, G_nodes.index(seed_i))) # NOTE: no guarantee that seed_i is in the clique if len(Q) > len(bestQ): bestQ = Q return bestQ
def clique_perfects(seed_i, cursor):
G = seed_graph(seed_i, cursor)
# first find a perfect clique of size >= PERFECT_CLIQUE_MIN_SIZE
G.delete_nodes_from( filter(lambda n: G.degree(n)<PERFECT_CLIQUE_MIN_SIZE, G.nodes_iter()) )
if not G.has_node(seed_i):
return []
print >> sys.stderr, "initial seed graph gets {0} nodes, {1} edges".format(\
G.number_of_nodes(),G.number_of_edges())
G_nodes = G.nodes()
S,H = p.convert_graph_connectivity_to_sparse(G, G_nodes)
remembered_perfects = []
# for PERFECT_MAXITR iterations, store all found perfect cliques containing seed_i
for itr in xrange(PERFECT_MAXITR):
alpha = uniform(.1, .9)
Q = map(lambda x: G_nodes[x], p.construct(S, H, alpha, G_nodes.index(seed_i)))
Q.sort() # sort it, so we add only distinct cliques
if seed_i in Q and len(Q) >= PERFECT_CLIQUE_MIN_SIZE\
and Q not in remembered_perfects:
remembered_perfects.append(Q)
if len(remembered_perfects) == 0:
return []
remembered_perfects.sort(reverse=True,key=lambda x:len(x))
return remembered_perfects[0]