def test1():
network = nx.karate_club_graph()
communities = qs.louvain(network)
sg, pvals, qhat, shat = qs.qstest(network, communities, qs.qmod, qs.vol,
qs.louvain)
assert len(sg) == len(communities)
assert len(pvals) == len(communities)
assert max(pvals) <= 1.0
assert min(pvals) >= 0.0
import networkx as nx
import qstest as qs
from networkx.algorithms import community as nxcdalgorithm
# Wrapper function for async_fluidc implemented in Networkx 2.0
def my_cdalgorithm(network):
communities = []
subnets = nx.connected_component_subgraphs(network)
for subnet in subnets:
coms_iter = nxcdalgorithm.asyn_fluidc(subnet, min([C, subnet.order()]), maxiter)
for nodes in iter(coms_iter):
communities.append(list(nodes))
return communities
# Pareameters of async_fluidc
C = 3
maxiter = 10
network = nx.karate_club_graph()
communities = my_cdalgorithm(network)
sg, p_values = qs.qstest(network, communities, qs.qmod, qs.vol, my_cdalgorithm)
print(" Subgraph has no edges.")
# Identify communities
print("** Finding communities (disease modules)...")
communities = louvain(g)
coms = community_louvain.best_partition(g)
C = max(coms.values()) + 1
print(" Done.")
# Check statistical significance of the communities over randomly generated communities
print("\n** Testing significance of the communities....")
stat_test = qs.qstest(g,
communities,
find_modularity,
find_size,
louvain,
num_of_rand_net=int(args.random),
num_of_thread=int(args.threads))
values = [list(x) for x in stat_test]
sg = values[0]
pval = values[1]
# Write significant communities to file
out = write_sig_comm_tofile(sg, pval, communities, args.out)
# Create logfile with miscellaneous details
ofile = os.path.join(args.out, 'find_cluster_logfile.txt')
outfile = open(ofile, 'w')
outfile.write(" ** Network Details:\n")
outfile.write(" Network contains %s nodes and %s edges\n" %
import networkx as nx
import qstest as qs
# Number of intra-community edges
def my_qfunc(network, nodes):
return network.subgraph(nodes).size()
network = nx.karate_club_graph()
communities = qs.louvain(network)
sg, p_values = qs.qstest(network, communities, my_qfunc, qs.vol, qs.louvain)