def cluster(self, verbose=False):
# data = defaultdict(Set) # node id => neighboring node ids
data = Dict()
# read in graph
with open(self.filename, 'r') as f:
counter = 0
for line in f:
a, b = line.split()[:2]
counter += 1
if a in data:
data[a].add(b)
else:
data[a] = Set()
data[a].add(b)
if b in data:
data[b].add(a)
else:
data[b] = Set()
data[b].add(a)
# weights = defaultdict(int)
weights = Dict()
for a, b in combinations(data, 2):
if b not in data[a]: continue
shared = len(data[a] & data[b])
if a in weights:
weights[a] += shared
else:
weights[a] = 0
weights[a] += shared
if b in weights:
weights[b] += shared
else:
weights[b] = 0
weights[b] += shared
unvisited = Set(data)
num_clusters = 0
clusters = []
# print(unvisited)
# return 0
# Potential culprit
seed_nodes = sorted(data,
key=lambda k: (weights[k], len(data[k])),
reverse=True)
for seed in seed_nodes: # get highest degree node
if seed not in unvisited: continue
cluster = Set(
(seed, next(iter(data[seed])))) # seed and random neighbor
while True:
# rank neighbors by the number of edges between the node and cluster nodes
frontier = sorted((len(data[p] & cluster), p)
for p in Set.union(*((data[n] - cluster)
for n in cluster)))
# do this until IN_vk < T_IN, SP <= 2 is met, or no frontier nodes left
found = False
while frontier and not found:
m_vk, p = frontier.pop()
if m_vk < self.t_in * len(cluster): break
c_2neighbors = data[p] & cluster
c_2neighbors.update(*(data[c] & cluster
for c in c_2neighbors))
if cluster == c_2neighbors:
found = True
break
if not found: break
# otherwise, add the node to the cluster
cluster.add(p)
unvisited -= cluster
if verbose:
print(' '.join(cluster))
num_clusters += 1
if verbose:
print(num_clusters, len(cluster), len(unvisited))
clusters.append(cluster)
if not unvisited: break
self.clusters = clusters
# if __name__ == '__main__':
# filename = "../data/unweighted_example_network.txt"
# c = IPCA(filename)
# c.cluster()
def ipca(filename):
data = defaultdict(Set) # node id => neighboring node ids
# read in graph
with open(filename, 'r') as f:
counter = 0
for line in f:
a,b = line.split()[:2]
print(a,b)
print(counter)
counter += 1
data[a].add(b)
data[b].add(a)
weights = defaultdict(int)
for a,b in combinations(data, 2):
if b not in data[a]: continue
shared = len(data[a] & data[b])
weights[a] += shared
weights[b] += shared
unvisited = Set(data)
num_clusters = 0
clusters = []
# Potential culprit
seed_nodes = sorted(data, key=lambda k: (weights[k],len(data[k])), reverse=True)
for seed in seed_nodes: # get highest degree node
if seed not in unvisited: continue
cluster = Set((seed,next(iter(data[seed])))) # seed and random neighbor
while True:
# rank neighbors by the number of edges between the node and cluster nodes
frontier = sorted((len(data[p] & cluster),p) for p in
Set.union(*((data[n] - cluster) for n in cluster)))
# do this until IN_vk < T_IN, SP <= 2 is met, or no frontier nodes left
found = False
while frontier and not found:
m_vk,p = frontier.pop()
if m_vk < T_IN * len(cluster): break
c_2neighbors = data[p] & cluster
c_2neighbors.update(*(data[c] & cluster for c in c_2neighbors))
if cluster == c_2neighbors:
found = True
break
if not found: break
# otherwise, add the node to the cluster
cluster.add(p)
unvisited -= cluster
print (' '.join(cluster))
num_clusters += 1
print (num_clusters, len(cluster), len(unvisited))
clusters.append(cluster)
if not unvisited: break