def timings():
N_range = [5, 10, 30, 50, 100, 200, 500, 1000, 10000]
N_range = [5, 10, 30, 50, 100, 200]
df = pd.DataFrame({}, columns=["N", "time"])
for N in N_range:
print N
G = nx.fast_gnp_random_graph(N, 10./N)
start_time = time()
kplexAlg(G, 2)
run_time = time()-start_time
print run_time
df.loc[len(df)] = [N, run_time]
df = df.set_index("N")
print df
def analyzeNetwork(G, k=2, filename=None):
# Get kplexes
if filename and isfile(filename):
kplexesMax = pickle.load(open(filename))
else:
_, kplexesMax = kplexAlg(G, k, verbose=True)
if filename:
pickle.dump(kplexesMax, open(filename, 'wb'))
print "List of {}-plexes".format(k)
print kplexesMax
# get histogram for size of kplex
kplexSizes = map(len, kplexesMax)
plt.hist(kplexSizes)