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community_detection.py
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community_detection.py
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from pyspark import SparkContext
import pyspark
import itertools
import sys
import time
def flatten_user_pair(i):
for pair in itertools.combinations(i[1], 2):
yield (tuple(sorted(pair)), 1)
def get_shortest_path(user, neighbors_map, result):
queue = [user, None]
d = dict() #key = child, value = (parent & #shortest path) for all parents
path_count = dict() # key = node, value = #shortest path
visited = set(user)
while queue:
curr_user = queue.pop(0)
if curr_user == None:
if d:
result.append(list(d.items()))
for key in d:
visited.add(key)
queue.append(key)
queue.append(None)
d = dict()
curr_user = queue.pop(0)
if curr_user in neighbors_map:
if curr_user not in path_count:
curr_user = (curr_user, 1)
else:
curr_user = (curr_user, path_count[curr_user])
for neighbor in neighbors_map[curr_user[0]]:
if neighbor not in visited:
if neighbor in d:
d[neighbor] += (curr_user,)
else:
d[neighbor] = (curr_user,)
path_count[neighbor] = path_count.get(neighbor, 0) + curr_user[1]
return (result, path_count)
def get_betweenness(user, neighbors_map):
shortest_path, path_count = get_shortest_path(user, neighbors_map, [])
d = {} #key = parent node, value = sum of betweenness of children
while shortest_path:
curr_level = shortest_path.pop(-1)
for t in curr_level:
child = t[0]
for parent in t[1]:
prev = 0
if child in d:
prev += d[child]
btn = (prev + 1) * (parent[1] / path_count[child])
d[parent[0]] = d.get(parent[0], 0) + btn
yield (tuple(sorted((parent[0], child))), btn)
def get_communities(users, neighbors_map):
communities = []
user_visited = set()
for user in users:
if user not in user_visited:
queue = [user]
visited = set([user])
user_visited.add(user)
while queue:
curr_user = queue.pop(0)
if curr_user in neighbors_map:
for neighbor in neighbors_map[curr_user]:
if neighbor not in visited:
user_visited.add(neighbor)
visited.add(neighbor)
queue.append(neighbor)
communities.append(tuple(sorted(visited)))
return communities
def get_modularity(pair, A, m, degrees_map):
if pair in A:
Aij = 1
else:
Aij = 0
useri, userj = pair
ki, kj = degrees_map[useri], degrees_map[userj]
return Aij - ((ki * kj) / (2 * m))
def get_best_communities(user1, user2, users, neighbors_map, degrees_map, m, A):
max_modularity = -1
user_count = users.count()
pre_modularity = -1
while True:
# delete neighbors in map
neighbors_map[user1].remove(user2)
neighbors_map[user2].remove(user1)
# find communities and calculate modularity
communities = get_communities(users.collect(), neighbors_map)
modularity = 0
for community in communities:
for pair in itertools.combinations(community, 2):
modularity += get_modularity(pair, A, m, degrees_map)
modularity /= (2 * m)
if modularity > max_modularity:
max_modularity = modularity
best_communities = communities
if len(communities) == user_count:
break
# calculate new betweenness
new_betweenness = users \
.flatMap(lambda user: get_betweenness(user, neighbors_map)) \
.reduceByKey(lambda x, y: x + y) \
.mapValues(lambda value: value / 2) \
.sortBy(lambda x: (-x[1], x[0]))
# cut the edge between user1 and user2 (max_betweenness)
user1, user2 = new_betweenness.take(1)[0][0]
return best_communities
def main():
start = time.time()
threshold = int(sys.argv[1])
input_file = sys.argv[2]
btn_output_file = sys.argv[3]
com_output_file = sys.argv[4]
sc = SparkContext()
sc.setLogLevel("ERROR")
edges = sc.textFile(input_file) \
.map(lambda line: line.split(',')) \
.map(lambda line: (line[1], line[0])) \
.groupByKey() \
.filter(lambda line: len(line[1]) >= 2) \
.flatMap(flatten_user_pair) \
.reduceByKey(lambda x, y: x + y) \
.filter(lambda line: line[1] >= threshold)
users = edges \
.flatMap(lambda line: [line[0][0], line[0][1]]) \
.distinct()
edges = edges \
.flatMap(lambda line: [line[0], (line[0][1], line[0][0])])
user_neighbors = edges.groupByKey()
neighbors_map = dict(user_neighbors \
.mapValues(set) \
.collect())
# Betweenness Caecueation
betweenness = users \
.flatMap(lambda user: get_betweenness(user, neighbors_map)) \
.reduceByKey(lambda x, y: x + y) \
.mapValues(lambda value: value / 2) \
.sortBy(lambda x: (-x[1], x[0]))
# Community Detection
degrees_map = dict(user_neighbors \
.mapValues(len) \
.collect())
A = set(betweenness.map(lambda line: line[0]).collect())
m = betweenness.count()
user1, user2 = betweenness.take(1)[0][0]
best_communities = get_best_communities(user1, user2, users, neighbors_map, degrees_map, m, A)
result_communities = sorted(best_communities, key = lambda community: (len(community), community))
# output results
result_betweenness = betweenness.collect()
fh = open(btn_output_file, 'w')
for line in result_betweenness:
fh.write(str(line).rstrip(')').replace('(', '', 1))
fh.write('\n')
fh.close()
fh = open(com_output_file, 'w')
for line in result_communities:
fh.write(str(line).replace('(', '').replace(',)', '').replace(')', ''))
fh.write('\n')
fh.close()
print("Duration: %s" % (time.time() - start))
if __name__ == "__main__":
main()