def get_pagerank(filename='data/fs_pagerank.pck'):
if os.path.isfile(filename):
return cPickle.load(open(filename, 'rb'))
else:
network = get_spatial_friendship_network()
pagerank = nx.pagerank(convert_network(network))
cPickle.dump(pagerank, open(filename, 'wb'))
return pagerank
def dump_network():
network = get_spatial_friendship_network()
locations = get_users_locations()
G = convert_network(network)
network_file = open('foursquare_network_private.txt', 'w')
for u, t in G.edges_iter():
network_file.write('%s %s\n' % (u, t))
location_file = open('foursquare_locations_private.txt', 'w')
for u, cord in locations.items():
lat, lng = cord
if u in G: location_file.write('%s %lf %lf\n' % (u, lat, lng))
for i in xrange(len(communities)):
val = [pagerank[node] for node in communities[i]]
results[i] = average(val)
return results
network_code = 1
community_code = 2
network, locations, pagerank, states = routing_simulation_loader.load_network(
network_code)
communities, com_table, cgraph, stationary = routing_simulation_loader.load_community(
network_code, community_code)
com_table = convert_com_table(communities)
com_pagerank = get_community_pagerank(communities)
G = convert_network(network)
for node, com in com_table.items():
for ci, cj in combinations(com, 2):
val = com_pagerank[ci] - com_pagerank[cj]
print ci, cj, val, abs(val)
for u, v in G.edges_iter():
com_u = com_table[u]
com_v = com_table[v]
for ci, cj in product(com_u, com_v):
if ci == cj: continue
val = com_pagerank[ci] - com_pagerank[cj]
#print ci, cj, val, abs(val)
def load_network(): network = foursquare.get_spatial_friendship_network() #network = gowalla.get_spatial_friendship_network() G = gowalla.convert_network(network) return network, G
def transitivity():
network = get_spatial_friendship_network()
G = convert_network(network)
print "Foursquare Transitivity %lf" % nx.transitivity(G)
def calc_pagerank(network, output_file):
G = convert_network(network)
pr = nx.pagerank(G, alpha=0.9)
cPickle.dump(network, open(output_file, 'wb'))
def network_size():
network = get_spatial_friendship_network()
G = convert_network(network)
print "Number of nodes: %d" % len(G.nodes())
print "Number of edges: %d" % len(G.edges())
if r1 != r2 and tup1 not in pairs and tup2 not in pairs:
pairs.add(tup1)
return list(pairs)
def calc_paths(pairs):
results = []
for starter, target in pairs:
sp = networkx.shortest_path(G, starter, target)
results.append((starter, target, len(sp)))
return results
# Step 1: Load the network into memory.
network = foursquare.get_spatial_friendship_network()
G = gowalla.convert_network(network)
users = network.keys()
# Step 2: Pick k randomly select pairs
num_pairs = 10000 # 10^4 pairs
pairs = select_pairs(num_pairs)
# Step 3: Map & Reduce the pair-wise computations
num_processors = 10
work_input = split_work(pairs, num_processors)
results = multiprocessing.Pool(num_processors).map(calc_paths, work_input)
# Step 4: Output the results
for sub_results in results:
for starter, target, hop in sub_results:
print starter, target, hop
