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main.py
516 lines (428 loc) · 16.7 KB
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main.py
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# The performance of the methods and the baselines given the number of sources
# on multiple graphs: Planetoid, ER, and SW
import warnings
warnings.filterwarnings("ignore")
from methods import *
import time
import statistics as s
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
print(diffusion_model)
################################################
# Global parameters
################################################
num_of_sims = 50
print('small world')
G = connSW(1000)
GPCS = []
CST = []
GPRS = []
RST = []
GPSI = []
SIT = []
JORDAN = []
JORDANT = []
LISN = []
LISNT = []
NET = []
NETT = []
for i in range(5):
s_star = create_true_source_set(G, num_of_sources=seed_size)
contagion = Contagion(G=G, model=diffusion_model, infection_rate=infect_rate, source=s_star)
contagion.forward(actual_time_step_size)
obs = contagion.get_infected_subgraph(step=actual_time_step_size-1)
c_star = list(obs.nodes)
start = time.time()
gpsi_cs_kmeans = GPSI_cluster_sampling(G, c_star, num_iterations, num_of_sims, infect_rate, candidate_size, diffusion_model, seed_size, number_of_clusters=20)
end = time.time()
CST.append(end - start)
dist = node_set_distance(gpsi_cs_kmeans, s_star, G)
GPCS.append(dist)
start = time.time()
gpsi_ft = GPSI_ft(G, c_star, num_iterations, num_of_sims, infect_rate, candidate_size, diffusion_model, seed_size)
end = time.time()
RST.append(end - start)
dist = node_set_distance(gpsi_ft, s_star, G)
GPRS.append(dist)
start = time.time()
gpsi_vanilla = GPSI_vanilla(G, c_star, num_iterations, num_of_sims, infect_rate, candidate_size, diffusion_model, seed_size)
end = time.time()
SIT.append(end - start)
dist = node_set_distance(gpsi_vanilla, s_star, G)
GPSI.append(dist)
start = time.time()
jordan = cosasi.source_inference.multiple_source.fast_multisource_jordan_centrality(obs, G, 3).topn(1)
end = time.time()
jordan_result = []
for j in jordan[0]:
jordan_result.append(j)
dist = node_set_distance(jordan_result, s_star, G)
JORDANT.append(end - start)
JORDAN.append(dist)
start = time.time()
lisn = cosasi.source_inference.multiple_source.fast_multisource_lisn(obs, G, actual_time_step_size, 3).topn((1))
end = time.time()
lisn_result = []
for j in lisn[0]:
lisn_result.append(j)
dist = node_set_distance(lisn_result, s_star, G)
LISNT.append(end - start)
LISN.append(dist)
start = time.time()
sleuth = cosasi.source_inference.multiple_source.fast_multisource_netsleuth(obs, G, number_sources=3).topn((1))
end = time.time()
sleuth_result = []
for j in sleuth[0]:
sleuth_result.append(j)
dist = node_set_distance(sleuth_result, s_star, G)
NETT.append(end - start)
NET.append(dist)
print('GPSI with fourier transfer and kmeans cluster sampling')
print('time: ', s.mean(CST), "+-", s.stdev(CST))
print('eval: ', s.mean(GPCS), '+-', s.stdev(GPCS))
print('GPSI with fourier transfer and random sampling')
print('time: ', s.mean(RST), "+-", s.stdev(RST))
print('eval: ', s.mean(GPRS), '+-', s.stdev(GPRS))
print('GPSI with vanilla sampling')
print('time: ', s.mean(SIT), "+-", s.stdev(SIT))
print('eval: ', s.mean(GPSI), '+-', s.stdev(GPSI))
print('Jordan sampling')
print('time: ', s.mean(JORDANT), "+-", s.stdev(JORDANT))
print('eval: ', s.mean(JORDAN), '+-', s.stdev(JORDAN))
print('LISN sampling')
print('time: ', s.mean(LISNT), "+-", s.stdev(LISNT))
print('eval: ', s.mean(LISN), '+-', s.stdev(LISN))
print('NET sampling')
print('time: ', s.mean(NETT), "+-", s.stdev(NETT))
print('eval: ', s.mean(NET), '+-', s.stdev(NET))
print('ER')
G = ER(1000)
GPCS = []
CST = []
GPRS = []
RST = []
GPSI = []
SIT = []
JORDAN = []
JORDANT = []
LISN = []
LISNT = []
NET = []
NETT = []
for i in range(5):
s_star = create_true_source_set(G, num_of_sources=seed_size)
contagion = Contagion(G=G, model=diffusion_model, infection_rate=infect_rate, source=s_star)
contagion.forward(actual_time_step_size)
obs = contagion.get_infected_subgraph(step=actual_time_step_size-1)
c_star = list(obs.nodes)
start = time.time()
gpsi_cs_kmeans = GPSI_cluster_sampling(G, c_star, num_iterations, num_of_sims, infect_rate, candidate_size, diffusion_model, seed_size, number_of_clusters=20)
end = time.time()
CST.append(end - start)
dist = node_set_distance(gpsi_cs_kmeans, s_star, G)
GPCS.append(dist)
start = time.time()
gpsi_ft = GPSI_ft(G, c_star, num_iterations, num_of_sims, infect_rate, candidate_size, diffusion_model, seed_size)
end = time.time()
RST.append(end - start)
dist = node_set_distance(gpsi_ft, s_star, G)
GPRS.append(dist)
start = time.time()
gpsi_vanilla = GPSI_vanilla(G, c_star, num_iterations, num_of_sims, infect_rate, candidate_size, diffusion_model, seed_size)
end = time.time()
SIT.append(end - start)
dist = node_set_distance(gpsi_vanilla, s_star, G)
GPSI.append(dist)
start = time.time()
jordan = cosasi.source_inference.multiple_source.fast_multisource_jordan_centrality(obs, G, 3).topn(1)
end = time.time()
jordan_result = []
for j in jordan[0]:
jordan_result.append(j)
dist = node_set_distance(jordan_result, s_star, G)
JORDANT.append(end - start)
JORDAN.append(dist)
start = time.time()
lisn = cosasi.source_inference.multiple_source.fast_multisource_lisn(obs, G, actual_time_step_size, 3).topn((1))
end = time.time()
lisn_result = []
for j in lisn[0]:
lisn_result.append(j)
dist = node_set_distance(lisn_result, s_star, G)
LISNT.append(end - start)
LISN.append(dist)
start = time.time()
sleuth = cosasi.source_inference.multiple_source.fast_multisource_netsleuth(obs, G, number_sources=3).topn((1))
end = time.time()
sleuth_result = []
for j in sleuth[0]:
sleuth_result.append(j)
dist = node_set_distance(sleuth_result, s_star, G)
NETT.append(end - start)
NET.append(dist)
print('GPSI with fourier transfer and kmeans cluster sampling')
print('time: ', s.mean(CST), "+-", s.stdev(CST))
print('eval: ', s.mean(GPCS), '+-', s.stdev(GPCS))
print('GPSI with fourier transfer and random sampling')
print('time: ', s.mean(RST), "+-", s.stdev(RST))
print('eval: ', s.mean(GPRS), '+-', s.stdev(GPRS))
print('GPSI with vanilla sampling')
print('time: ', s.mean(SIT), "+-", s.stdev(SIT))
print('eval: ', s.mean(GPSI), '+-', s.stdev(GPSI))
print('Jordan sampling')
print('time: ', s.mean(JORDANT), "+-", s.stdev(JORDANT))
print('eval: ', s.mean(JORDAN), '+-', s.stdev(JORDAN))
print('LISN sampling')
print('time: ', s.mean(LISNT), "+-", s.stdev(LISNT))
print('eval: ', s.mean(LISN), '+-', s.stdev(LISN))
print('NET sampling')
print('time: ', s.mean(NETT), "+-", s.stdev(NETT))
print('eval: ', s.mean(NET), '+-', s.stdev(NET))
print('Cora')
G = Cora()
GPCS = []
CST = []
GPRS = []
RST = []
GPSI = []
SIT = []
JORDAN = []
JORDANT = []
LISN = []
LISNT = []
NET = []
NETT = []
for i in range(5):
s_star = create_true_source_set(G, num_of_sources=seed_size)
contagion = Contagion(G=G, model=diffusion_model, infection_rate=infect_rate, source=s_star)
contagion.forward(actual_time_step_size)
obs = contagion.get_infected_subgraph(step=actual_time_step_size-1)
c_star = list(obs.nodes)
start = time.time()
gpsi_cs_kmeans = GPSI_cluster_sampling(G, c_star, num_iterations, num_of_sims, infect_rate, candidate_size, diffusion_model, seed_size, number_of_clusters=20)
end = time.time()
CST.append(end - start)
dist = node_set_distance(gpsi_cs_kmeans, s_star, G)
GPCS.append(dist)
start = time.time()
gpsi_ft = GPSI_ft(G, c_star, num_iterations, num_of_sims, infect_rate, candidate_size, diffusion_model, seed_size)
end = time.time()
RST.append(end - start)
dist = node_set_distance(gpsi_ft, s_star, G)
GPRS.append(dist)
start = time.time()
gpsi_vanilla = GPSI_vanilla(G, c_star, num_iterations, num_of_sims, infect_rate, candidate_size, diffusion_model, seed_size)
end = time.time()
SIT.append(end - start)
dist = node_set_distance(gpsi_vanilla, s_star, G)
GPSI.append(dist)
start = time.time()
jordan = cosasi.source_inference.multiple_source.fast_multisource_jordan_centrality(obs, G, 3).topn(1)
end = time.time()
jordan_result = []
for j in jordan[0]:
jordan_result.append(j)
dist = node_set_distance(jordan_result, s_star, G)
JORDANT.append(end - start)
JORDAN.append(dist)
start = time.time()
lisn = cosasi.source_inference.multiple_source.fast_multisource_lisn(obs, G, actual_time_step_size, 3).topn((1))
end = time.time()
lisn_result = []
for j in lisn[0]:
lisn_result.append(j)
dist = node_set_distance(lisn_result, s_star, G)
LISNT.append(end - start)
LISN.append(dist)
start = time.time()
sleuth = cosasi.source_inference.multiple_source.fast_multisource_netsleuth(obs, G, number_sources=3).topn((1))
end = time.time()
sleuth_result = []
for j in sleuth[0]:
sleuth_result.append(j)
dist = node_set_distance(sleuth_result, s_star, G)
NETT.append(end - start)
NET.append(dist)
print('GPSI with fourier transfer and kmeans cluster sampling')
print('time: ', s.mean(CST), "+-", s.stdev(CST))
print('eval: ', s.mean(GPCS), '+-', s.stdev(GPCS))
print('GPSI with fourier transfer and random sampling')
print('time: ', s.mean(RST), "+-", s.stdev(RST))
print('eval: ', s.mean(GPRS), '+-', s.stdev(GPRS))
print('GPSI with vanilla sampling')
print('time: ', s.mean(SIT), "+-", s.stdev(SIT))
print('eval: ', s.mean(GPSI), '+-', s.stdev(GPSI))
print('Jordan sampling')
print('time: ', s.mean(JORDANT), "+-", s.stdev(JORDANT))
print('eval: ', s.mean(JORDAN), '+-', s.stdev(JORDAN))
print('LISN sampling')
print('time: ', s.mean(LISNT), "+-", s.stdev(LISNT))
print('eval: ', s.mean(LISN), '+-', s.stdev(LISN))
print('NET sampling')
print('time: ', s.mean(NETT), "+-", s.stdev(NETT))
print('eval: ', s.mean(NET), '+-', s.stdev(NET))
print('CiteSeer')
G = CiteSeer()
GPCS = []
CST = []
GPRS = []
RST = []
GPSI = []
SIT = []
JORDAN = []
JORDANT = []
LISN = []
LISNT = []
NET = []
NETT = []
for i in range(5):
s_star = create_true_source_set(G, num_of_sources=seed_size)
contagion = Contagion(G=G, model=diffusion_model, infection_rate=infect_rate, source=s_star)
contagion.forward(actual_time_step_size)
obs = contagion.get_infected_subgraph(step=actual_time_step_size-1)
c_star = list(obs.nodes)
start = time.time()
gpsi_cs_kmeans = GPSI_cluster_sampling(G, c_star, num_iterations, num_of_sims, infect_rate, candidate_size, diffusion_model, seed_size, number_of_clusters=20)
end = time.time()
CST.append(end - start)
dist = node_set_distance(gpsi_cs_kmeans, s_star, G)
GPCS.append(dist)
start = time.time()
gpsi_ft = GPSI_ft(G, c_star, num_iterations, num_of_sims, infect_rate, candidate_size, diffusion_model, seed_size)
end = time.time()
RST.append(end - start)
dist = node_set_distance(gpsi_ft, s_star, G)
GPRS.append(dist)
start = time.time()
gpsi_vanilla = GPSI_vanilla(G, c_star, num_iterations, num_of_sims, infect_rate, candidate_size, diffusion_model, seed_size)
end = time.time()
SIT.append(end - start)
dist = node_set_distance(gpsi_vanilla, s_star, G)
GPSI.append(dist)
start = time.time()
jordan = cosasi.source_inference.multiple_source.fast_multisource_jordan_centrality(obs, G, 3).topn(1)
end = time.time()
jordan_result = []
for j in jordan[0]:
jordan_result.append(j)
dist = node_set_distance(jordan_result, s_star, G)
JORDANT.append(end - start)
JORDAN.append(dist)
start = time.time()
lisn = cosasi.source_inference.multiple_source.fast_multisource_lisn(obs, G, actual_time_step_size, 3).topn((1))
end = time.time()
lisn_result = []
for j in lisn[0]:
lisn_result.append(j)
dist = node_set_distance(lisn_result, s_star, G)
LISNT.append(end - start)
LISN.append(dist)
start = time.time()
sleuth = cosasi.source_inference.multiple_source.fast_multisource_netsleuth(obs, G, number_sources=3).topn((1))
end = time.time()
sleuth_result = []
for j in sleuth[0]:
sleuth_result.append(j)
dist = node_set_distance(sleuth_result, s_star, G)
NETT.append(end - start)
NET.append(dist)
print('GPSI with fourier transfer and kmeans cluster sampling')
print('time: ', s.mean(CST), "+-", s.stdev(CST))
print('eval: ', s.mean(GPCS), '+-', s.stdev(GPCS))
print('GPSI with fourier transfer and random sampling')
print('time: ', s.mean(RST), "+-", s.stdev(RST))
print('eval: ', s.mean(GPRS), '+-', s.stdev(GPRS))
print('GPSI with vanilla sampling')
print('time: ', s.mean(SIT), "+-", s.stdev(SIT))
print('eval: ', s.mean(GPSI), '+-', s.stdev(GPSI))
print('Jordan sampling')
print('time: ', s.mean(JORDANT), "+-", s.stdev(JORDANT))
print('eval: ', s.mean(JORDAN), '+-', s.stdev(JORDAN))
print('LISN sampling')
print('time: ', s.mean(LISNT), "+-", s.stdev(LISNT))
print('eval: ', s.mean(LISN), '+-', s.stdev(LISN))
print('NET sampling')
print('time: ', s.mean(NETT), "+-", s.stdev(NETT))
print('eval: ', s.mean(NET), '+-', s.stdev(NET))
print('Pubmed')
G = PubMed()
GPCS = []
CST = []
GPRS = []
RST = []
GPSI = []
SIT = []
JORDAN = []
JORDANT = []
LISN = []
LISNT = []
NET = []
NETT = []
for i in range(5):
s_star = create_true_source_set(G, num_of_sources=seed_size)
contagion = Contagion(G=G, model=diffusion_model, infection_rate=infect_rate, source=s_star)
contagion.forward(actual_time_step_size)
obs = contagion.get_infected_subgraph(step=actual_time_step_size-1)
c_star = list(obs.nodes)
start = time.time()
gpsi_cs_kmeans = GPSI_cluster_sampling(G, c_star, num_iterations, num_of_sims, infect_rate, candidate_size, diffusion_model, seed_size, number_of_clusters=20)
end = time.time()
CST.append(end - start)
dist = node_set_distance(gpsi_cs_kmeans, s_star, G)
GPCS.append(dist)
start = time.time()
gpsi_ft = GPSI_ft(G, c_star, num_iterations, num_of_sims, infect_rate, candidate_size, diffusion_model, seed_size)
end = time.time()
RST.append(end - start)
dist = node_set_distance(gpsi_ft, s_star, G)
GPRS.append(dist)
start = time.time()
gpsi_vanilla = GPSI_vanilla(G, c_star, num_iterations, num_of_sims, infect_rate, candidate_size, diffusion_model, seed_size)
end = time.time()
SIT.append(end - start)
dist = node_set_distance(gpsi_vanilla, s_star, G)
GPSI.append(dist)
start = time.time()
jordan = cosasi.source_inference.multiple_source.fast_multisource_jordan_centrality(obs, G, 3).topn(1)
end = time.time()
jordan_result = []
for j in jordan[0]:
jordan_result.append(j)
dist = node_set_distance(jordan_result, s_star, G)
JORDANT.append(end - start)
JORDAN.append(dist)
start = time.time()
lisn = cosasi.source_inference.multiple_source.fast_multisource_lisn(obs, G, actual_time_step_size, 3).topn((1))
end = time.time()
lisn_result = []
for j in lisn[0]:
lisn_result.append(j)
dist = node_set_distance(lisn_result, s_star, G)
LISNT.append(end - start)
LISN.append(dist)
start = time.time()
sleuth = cosasi.source_inference.multiple_source.fast_multisource_netsleuth(obs, G, number_sources=3).topn((1))
end = time.time()
sleuth_result = []
for j in sleuth[0]:
sleuth_result.append(j)
dist = node_set_distance(sleuth_result, s_star, G)
NETT.append(end - start)
NET.append(dist)
print('GPSI with fourier transfer and kmeans cluster sampling')
print('time: ', s.mean(CST), "+-", s.stdev(CST))
print('eval: ', s.mean(GPCS), '+-', s.stdev(GPCS))
print('GPSI with fourier transfer and random sampling')
print('time: ', s.mean(RST), "+-", s.stdev(RST))
print('eval: ', s.mean(GPRS), '+-', s.stdev(GPRS))
print('GPSI with vanilla sampling')
print('time: ', s.mean(SIT), "+-", s.stdev(SIT))
print('eval: ', s.mean(GPSI), '+-', s.stdev(GPSI))
print('Jordan sampling')
print('time: ', s.mean(JORDANT), "+-", s.stdev(JORDANT))
print('eval: ', s.mean(JORDAN), '+-', s.stdev(JORDAN))
print('LISN sampling')
print('time: ', s.mean(LISNT), "+-", s.stdev(LISNT))
print('eval: ', s.mean(LISN), '+-', s.stdev(LISN))
print('NET sampling')
print('time: ', s.mean(NETT), "+-", s.stdev(NETT))
print('eval: ', s.mean(NET), '+-', s.stdev(NET))