def top_rank(k,rank):
pq = PriorityQueue()
for u in rank.keys():
pq.add(u, -rank[u]) # We use negative value because PriorityQueue returns first values whose priority value is lower
out=[]
for i in range(k):
out.append(pq.pop())
return out
def top(G,measure,k):
pq = PriorityQueue()
cen=measure(G)
for u in G.nodes():
pq.add(u, -cen[u]) # We use negative value because PriorityQueue returns first values whose priority value is lower
out=[]
for i in range(k):
out.append(pq.pop())
return out
def top_hits_parall(G,k,num_node,j):
pq = PriorityQueue()
pq2=PriorityQueue()
auth_n,hubs_n=parallel_hits(G,k,j)
for u in G.nodes():
pq.add(u, -auth_n[u]) # We use negative value because PriorityQueue returns first values whose priority value is lower
for u in G.nodes():
pq2.add(u, -hubs_n[u]) # We use negative value because PriorityQueue returns first values whose priority value is lower
out=[]
out2=[]
for i in range(num_node):
out.append(pq.pop())
out2.append(pq2.pop())
return out,out2
def top_parallel(G,k,j):
pq = PriorityQueue()
with Parallel(n_jobs=j) as parallel:
#Run in parallel diameter function on each processor by passing to each processor only the subset of nodes on which it works
result=parallel(delayed(closeness_par)(G,X) for X in chunks(G.nodes(), math.ceil(len(G.nodes())/j)))
for u in result:#u is a dict
for el in u.keys():
pq.add(el, -u[el])
# We use negative value because PriorityQueue returns first values whose priority value is lower
out=[]
for i in range(k):
out.append(pq.pop())
return out
def top_betweenness(G,k,j):
#PARALLELIZZAZIONE
pq=PriorityQueue()
with Parallel(n_jobs=j) as parallel:
#Run in parallel diameter function on each processor by passing to each processor only the subset of nodes on which it works
lista=parallel(delayed(betweenness_par)(G,X) for X in chunks(G.nodes(), math.ceil(len(G.nodes())/j)))
#Aggregates the results
for j in lista:
for i in j[1].keys():
pq.add(i,-j[1][i])#Fase di assemblaggio
out=[]
for i in range(k):
out.append(pq.pop())
return out
def bwt_cluster_naive(G):
eb, nb = betweenness(G)
pq = PriorityQueue()
for i in eb.keys():
pq.add(i, -eb[i])
graph = G.copy()
done = False
while not done:
edge = tuple(sorted(pq.pop()))
graph.remove_edges_from([edge])
list_connected_comp = list(nx.connected_components(graph))
if len(list(nx.connected_components(graph))) == 4:
done = True
return list_connected_comp
def bwt_cluster_parallel(G,j):
#PARALLELIZZAZIONE
pq=PriorityQueue()
with Parallel(n_jobs=j) as parallel:
#Run in parallel diameter function on each processor by passing to each processor only the subset of nodes on which it works
lista=parallel(delayed(betweenness_par)(G,X) for X in chunks(G.nodes(), math.ceil(len(G.nodes())/j)))
#Aggregates the results
for j in lista:
for i in j[0].keys():
pq.add(i,-j[0][i])#Fase di assemblaggio
graph=G.copy()
done=False
while not done:
edge=tuple(sorted(pq.pop()))
graph.remove_edges_from([edge])
list_connected_comp=list(nx.connected_components(graph))
if len(list(nx.connected_components(graph))) == 4:
done = True
return list_connected_comp
def hierarchical(G,sample=None):
if sample is None:
sample=G.nodes()
# Create a priority queue with each pair of nodes indexed by distance
pq = PriorityQueue()
for u in sample:
for v in sample:
if u != v:
if (u, v) in G.edges() or (v, u) in G.edges():
pq.add(frozenset([frozenset([u]), frozenset([v])]), 0)
else:
pq.add(frozenset([frozenset([u]), frozenset([v])]), 1)
# Start with a cluster for each node
clusters = set(frozenset([u]) for u in sample)
done = False
while not done:
# Merge closest clusters
s = list(pq.pop())
clusters.remove(s[0])
clusters.remove(s[1])
# Update the distance of other clusters from the merged cluster
for w in clusters:
e1 = pq.remove(frozenset([s[0], w]))
e2 = pq.remove(frozenset([s[1], w]))
if e1 == 0 or e2 == 0:
pq.add(frozenset([s[0] | s[1], w]), 0)
else:
pq.add(frozenset([s[0] | s[1], w]), 1)
clusters.add((s[0] | s[1]))
if len(clusters) ==4:
done = True
return clusters