def q3():
harmonic = []
loc_glob = []
for mu in np.arange(0.1, 1, 0.1):
graph = LFR_benchmark_graph(1000,
3,
1.5,
mu=mu,
min_community=20,
average_degree=5,
seed=3)
nodes = graph.nodes
communities = {frozenset(graph.nodes[v]['community']) for v in graph}
res = np.zeros(len(nodes)).astype(int)
for i, part in enumerate(communities):
for j in part:
res[j] = i
harmonic_accs = []
local_global_accs = []
for i in range(10):
harmonic_acc, local_global_acc = drop_label(graph.copy(), res, 0.8)
harmonic_accs.append(harmonic_acc)
local_global_accs.append(local_global_acc)
harmonic.append(np.mean(harmonic_accs))
loc_glob.append(np.mean(local_global_accs))
for i in harmonic:
print("{:.4f}".format(i))
print()
for i in loc_glob:
print("{:.4f}".format(i))
print("Generating communities..")
n = 500
tau1 = 4
tau2 = 1.5
mu = 0.1
# _RANK_GRAPH = nx.windmill_graph(20, 5)
_RANK_GRAPH = LFR_benchmark_graph(n,
tau1,
tau2,
mu,
average_degree=5,
min_community=30,
seed=10)
print(nx.info(_RANK_GRAPH))
A = _RANK_GRAPH.copy()
_RANK_GRAPH = nx.to_scipy_sparse_matrix(_RANK_GRAPH)
_RANK_GRAPH = stochastic_normalization(_RANK_GRAPH) ## normalize
n = _RANK_GRAPH.shape[1]
with mp.Pool(processes=mp.cpu_count()) as p:
results = p.map(page_rank_kernel, range(n))
vectors = np.zeros((n, n))
for pr_vector in results:
if pr_vector != None:
vectors[pr_vector[0], :] = pr_vector[1]
vectors = np.nan_to_num(vectors)
option = "cpu"
dx_rc = defaultdict(list)
dx_lx = defaultdict(list)
