def predict(G, u, v):
if G.is_directed():
return sum(1 / log(G.degree(w))
for w in nx.directed_common_neighbors(G, u, v))
else:
return sum(1 / log(G.degree(w))
for w in nx.common_neighbors(G, u, v))
def predict(G, u, v):
if G.is_directed():
return len(list(nx.directed_common_neighbors(
G, u, v))) * (dict_ce[u][1] + dict_ce[v][2])
else:
return len(list(nx.common_neighbors(
G, u, v))) * (dict_ce[u][0] + dict_ce[v][0])
def predict(G, u, v):
if G.is_directed():
union_size = len(set(G._succ[u]) | set(G._pred[v]))
if union_size == 0:
return 0
return len(list(nx.directed_common_neighbors(G, u,
v))) / union_size
else:
union_size = len(set(G[u]) | set(G[v]))
if union_size == 0:
return 0
return len(list(nx.common_neighbors(G, u, v))) / union_size
def predict(G, u, v):
if G.is_directed():
return sum(dict_ce[w][0]
for w in nx.directed_common_neighbors(G, u, v))
else:
return sum(dict_ce[w][0] for w in nx.common_neighbors(G, u, v))
def predict(G, u, v):
if G.is_directed():
return len(list(nx.directed_common_neighbors(G, u, v)))
else:
return len(list(nx.common_neighbors(G, u, v)))