def test_create_random_graph(): base_dir = str(Path(__file__).parent/"examples") in_mat = np.load(base_dir + '/997/997_Default_est_cov_0.1_4.npy') G = nx.from_numpy_array(in_mat) n = 10 p = 0.5 start_time = time.time() rG = netstats.create_random_graph(G, n, p) print("%s%s%s" % ('thresh_and_fit (Functional, proportional thresholding) --> finished: ', str(np.round(time.time() - start_time, 1)), 's')) assert rG is not None
def smallworldness(G, rep = 1000): n = nx.number_of_nodes(G) m = nx.number_of_edges(G) p = float(m) * 2 /(n*(n-1)) ss = [] for bb in range(rep): rG = create_random_graph(G, n, p) swm = smallworldness_measure(G, rG) ss.append(swm) mean_s = np.mean(ss) return mean_s