def load_synthetic(mu, n=1000, tau1=3, tau2=1.5, edge_drop_percent=0.2):
G = LFR_benchmark_graph(n,
tau1,
tau2,
mu,
average_degree=5,
min_community=30,
seed=10)
for n in G.nodes:
G.nodes[n]['value'] = list(G.nodes[n]['community'])[0]
true_coms = list(nx.get_node_attributes(G, 'value').values())
com_keys = list(Counter(true_coms).keys())
for i in range(0, len(true_coms)):
G.nodes[i]['value'] = com_keys.index(true_coms[i])
#remove self edges
selfE = list(G.selfloop_edges())
for (i, j) in selfE:
G.remove_edge(i, j)
#convert all graph to undirected
G = nx.Graph(G)
ListOfNodes = list(G.nodes())
sample = int(len(ListOfNodes) * node_drop_percent)
RandomSample = random.sample(ListOfNodes, sample)
for n in G.nodes():
if (n not in RandomSample):
G.nodes[n][label_name] = G.nodes[n]['value']
return (G, RandomSample)
def load_synthetic(n=250, tau1=3, tau2=1.5, mu=0.1):
G = LFR_benchmark_graph(n, tau1, tau2, mu, average_degree=5, min_community=20, seed=10)
for n in G.nodes:
G.nodes[n]['value'] = list(G.nodes[n]['community'])[0]
true_coms = list(nx.get_node_attributes(G,'value').values())
com_keys = list(Counter(true_coms).keys())
for i in range(0, len(true_coms)):
G.nodes[i]['value'] = com_keys.index(true_coms[i])
true_labels = list(nx.get_node_attributes(G,'value').values())
true_labels = [ int(x) for x in true_labels ]
#remove self edges
selfE = list(G.selfloop_edges())
for (i,j) in selfE:
G.remove_edge(i,j)
#convert all graph to undirected
G = nx.Graph(G)
nG = nx.Graph(G)
# first convert the networkx graph to igraph
G = ig.Graph.Adjacency((nx.to_numpy_matrix(G) > 0).tolist())
G.to_undirected()
return (G, nG, true_labels)
