def test_random_reference():
G = nx.connected_watts_strogatz_graph(50, 6, 0.1, seed=rng)
Gr = random_reference(G, niter=1, seed=rng)
C = nx.average_clustering(G)
Cr = nx.average_clustering(Gr)
assert_true(C > Cr)
assert_raises(nx.NetworkXError, random_reference, nx.Graph())
assert_raises(nx.NetworkXNotImplemented, random_reference, nx.DiGraph())
H = nx.Graph(((0, 1), (2, 3)))
Hl = random_reference(H, niter=1, seed=rng)
def _get_small_world_measures(graph, meas, **kwargs):
# Compute the mean clustering coefficient and average shortest path length
# for an equivalent random graph
nrand = kwargs['nrand']
niter = kwargs['niter']
C = meas['C']
L = meas['L']
randMetrics = {"Cr": [], "Cl": [], "L": []}
for _ in range(nrand):
Gr = nx.random_reference(graph, niter=niter)
Gl = nx.lattice_reference(graph, niter=niter)
randMetrics["Cl"].append(nx.transitivity(Gl))
randMetrics["Cr"].append(nx.transitivity(Gr))
randMetrics["L"].append(nx.average_shortest_path_length(Gr))
Cl = np.mean(randMetrics["Cl"])
Cr = np.mean(randMetrics["Cr"])
Lr = np.mean(randMetrics["L"])
omega = (Lr / L) - (C / Cl)
sigma = (C / Cr) / (L / Lr)
meas['Omega'] = omega
meas['Sigma'] = sigma
for i, C in enumerate((G.subgraph(c).copy() for c in nx.connected_components(G))):
print(i, nx.average_shortest_path_length(C))
# %%
# so there is actually a built in function to test small-worldness in NetworkX.
# It might be fun to do the calculation myself and compare it to the results of
# built in. The built in is making my machine work... (didn't finish)
# nx.sigma(G)
# nx.omega(G)
# %%
largest_cc = max(nx.connected_components(G), key=len)
S = G.subgraph(largest_cc)
# %%
print(G.number_of_nodes())
print(S.number_of_nodes())
# %%
R = nx.random_reference(S)
# %%
L = nx.lattice_reference(S)
# %%
l = nx.average_shortest_path_length(S)
l_r = nx.average_shortest_path_length(R)
l_l = nx.average_shortest_path_length(L)
# %%
c = nx.average_clustering(S)
c_r = nx.average_clustering(R)
c_l = nx.average_clustering(L)
# %%
s = (c / c_r) / (l / l_r)
s
listOfWeightedGraphs = []
listOfUnweightedGraphs = []
for i in range(0, 20):
# Create an empty graph object for erdos renyi algorithm
g = nx.Graph()
# Adding nodes
g.add_nodes_from(range(1, N + 1))
erdos_renyi(g, P)
listOfWeightedGraphs.append(g)
#create new graph for maslov snepen
g1 = nx.Graph()
g1.add_nodes_from(range(1, N + 1))
create_unweighted_random_graph(g1, P)
g1 = nx.random_reference(g, 1, True, 1)
listOfUnweightedGraphs.append(g1)
# plot created weighted and unweighted graphs respectively.
nx.draw(g, with_labels=1)
plt.title("Example visualization of Erdos-Renyi graph")
plt.show()
nx.draw(g1, with_labels=1)
plt.title("Example visualization of Maslov-Snepen graph")
plt.show()
list_of_weighted_cms_lists = []
list_of_unweighted_cms_lists = []
# Centrality measurements for weighted graphs
