def main():
test_network = ["karate_club", "facebook"]
for net in test_network:
net_file = data_utils.get_data_path(net)
g = graph_utils.load_basic_network(net_file)
sir_file = net_file.split('.')[0] + '-sir.txt'
sir = {}
if os.path.exists(sir_file):
with open(sir_file, 'r') as f:
for l in f:
data = l.split()
id = int(data[0])
score = float(data[1])
sir[id] = score
else:
print("SIR Simulation start.")
sir = sir_ranking(g, gamma=1.0, num_epoch=100)
print("SIR Simulation end.")
centralities = [
nx.degree_centrality, nx.closeness_centrality,
nx.eigenvector_centrality, nx.pagerank, Local_gravity_model
]
for c in centralities:
if c.__name__ == 'pagerank':
res = c(g, alpha=0.95)
elif c.__name__ == 'Local_gravity_model':
res = c(g, depth=2)
else:
res = c(g)
tau, p = kendallTau(res, sir)
print("%s\t%s\t%f" % (net, c.__name__, tau))
def explore_lyb():
net_file = data_utils.get_data_path("lyb")
g = graph_utils.load_basic_network(net_file)
n2v_emb = node2vec.node2vec_emb(g, p=0.5, q=2, out_dim=32, num_walks=20)
n2v_emb.learn_embedding()
# out_file = net_file.split('.')[0]+'-n2v_emb.txt'
# n2v_emb.output_embedding(out_file)
node_labels = graph_utils.load_node_labels(data_utils.get_node_path("lyb"))
sus_best = n2v_emb.model.most_similar(positive=['23'])
for item in sus_best:
print(node_labels[int(item[0])], item[1])
com = list(greedy_modularity_communities(g))
node_community = {}
for i, c in enumerate(com):
for node in c:
node_community[node_labels[node]] = i
print(node_community)
network_stats(g, 'BA-Network')
# WS-Network
k = 20
p = 0.01
g = nx.watts_strogatz_graph(n, k, p)
g = g.to_undirected()
draw_degree_dist(g, k, title='WS-Network Distribution')
network_stats(g, 'WS-Network')
# powerlaw_cluster network
m = 10
p = 1
g = nx.powerlaw_cluster_graph(n, m, p)
g = g.to_undirected()
draw_degree_dist(g, m, title='PC-Network Distribution')
network_stats(g, 'PC-Network')
# Facebook Network
import os, sys
projct_root_path = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(projct_root_path)
import common.graph_utils as graph_utils
import common.data_utils as data_utils
net_file = data_utils.get_data_path("facebook")
g = graph_utils.load_basic_network(net_file)
g = g.to_undirected()
draw_degree_dist(g, m, title='Facebook-Network Distribution')
network_stats(g, 'Facebook-Network')
def main():
net_file = data_utils.get_data_path("lyb")
g = graph_utils.load_basic_network(net_file)
n2v_emb = node2vec.node2vec_emb(g, p=0.5, q=2, out_dim=64, num_walks=20)
n2v_emb.learn_embedding()