def sensitive_3():
top_50 = []
f = open("../data/univ_top_50_cs.txt", "r")
for line in f:
line = line.strip().lower()
top_50.append(line)
f.close()
fo = open(
"../result/result_top50_cs_newdata_apr09/sensitivity/all/sensitivity_diff_hits_weighted-inedge1.csv",
"w")
node_list, edge_list = dp.read_data("../data/data_top50_cs_apr09.csv",
self_edge=False)
G = dp.construct_graph(node_list, edge_list)
hits = algo.weighted_HITS(G, max_iterations=100, min_delta=0.00001)
result = sorted(hits.iteritems(), key=lambda asd: asd[1], reverse=True)
G.clear()
rank = []
for e in result:
if e[0] in top_50:
rank.append(e[0])
original_r = []
for e in result:
if e[0] in top_50:
original_r.append([e[0]])
for k in range(len(original_r)):
# if not original_r[k][0] == "mit":
node_list, edge_list = dp.read_data("../data/data_top50_cs_apr09.csv",
self_edge=False)
G = dp.construct_graph(node_list, edge_list)
G = remove_significant_edge(
G, original_r[k][0],
rank=rank) ### add one edge from MIT to <node>
hits = algo.weighted_HITS(G, max_iterations=100, min_delta=0.00001)
result = sorted(hits.iteritems(), key=lambda asd: asd[1], reverse=True)
#result = sorted(hits.iteritems(), key = lambda asd:asd[1], reverse = True)
G.clear()
res1 = []
for e in result:
if e[0] in top_50:
res1.append(e[0])
kr = 0
for i in range(len(res1)):
if res1[i] == original_r[k][0]:
kr = i
original_r[k].append(k - kr)
print original_r
fo.write("univ,diff+mit1\n")
for r in original_r:
for i in range(len(r)):
if i == 0:
fo.write(str(r[i]))
else:
fo.write("," + str(r[i]))
fo.write("\n")
fo.close()
def sensitive_3():
top_50 = []
f = open("../data/univ_top_50_cs.txt","r")
for line in f:
line = line.strip().lower()
top_50.append(line)
f.close()
fo = open("../result/result_top50_cs_newdata_apr09/sensitivity/all/sensitivity_diff_hits_weighted-inedge1.csv","w")
node_list, edge_list = dp.read_data("../data/data_top50_cs_apr09.csv", self_edge = False)
G = dp.construct_graph(node_list, edge_list)
hits = algo.weighted_HITS(G, max_iterations = 100, min_delta = 0.00001)
result = sorted(hits.iteritems(), key = lambda asd:asd[1], reverse = True)
G.clear()
rank = []
for e in result:
if e[0] in top_50:
rank.append(e[0])
original_r = []
for e in result:
if e[0] in top_50:
original_r.append([e[0]])
for k in range(len(original_r)):
# if not original_r[k][0] == "mit":
node_list, edge_list = dp.read_data("../data/data_top50_cs_apr09.csv", self_edge = False)
G = dp.construct_graph(node_list, edge_list)
G = remove_significant_edge(G, original_r[k][0], rank = rank) ### add one edge from MIT to <node>
hits = algo.weighted_HITS(G, max_iterations = 100, min_delta = 0.00001)
result = sorted(hits.iteritems(), key = lambda asd:asd[1], reverse = True)
#result = sorted(hits.iteritems(), key = lambda asd:asd[1], reverse = True)
G.clear()
res1 = []
for e in result:
if e[0] in top_50:
res1.append(e[0])
kr = 0
for i in range(len(res1)):
if res1[i] == original_r[k][0]:
kr = i
original_r[k].append(k-kr)
print original_r
fo.write("univ,diff+mit1\n")
for r in original_r:
for i in range(len(r)):
if i == 0:
fo.write(str(r[i]))
else:
fo.write(","+str(r[i]))
fo.write("\n")
fo.close()
def choose_algorithm(G, type="hits_weighted"):
result = []
if type == "hits_weighted":
result = algo.weighted_HITS(G, max_iterations=100, min_delta=0.00001)
#print "hits_weighted", result
elif type == "hits_hubavg":
result = algo.hubavg_HITS(G, max_iterations=100, min_delta=0.00001)
#print "hits_hubavg", result
elif type == "weightedPR_w_norm":
result = algo.weighted_PR_wnorm(G,
damping_factor=0.85,
max_iterations=100,
min_delta=0.00001)
#print "weightedPR_w_norm", result
elif type == "weightedPR_wo_norm":
result = algo.weighted_PR_wonorm(G,
damping_factor=0.85,
max_iterations=100,
min_delta=0.00001)
#print "weightedPR_wo_norm", result
elif type == "indegree":
result = dp.rank_univ_indegree(G)
#print "indegree", result
else:
print "error type!"
return result
def choose_algorithm(G, type = "hits_weighted"):
result = []
if type == "hits_weighted":
result = algo.weighted_HITS(G, max_iterations = 100, min_delta = 0.00001)
#print "hits_weighted", result
elif type == "hits_hubavg":
result = algo.hubavg_HITS(G, max_iterations = 100, min_delta = 0.00001)
#print "hits_hubavg", result
elif type == "weightedPR_w_norm":
result = algo.weighted_PR_wnorm(G, damping_factor = 0.85, max_iterations = 100, min_delta = 0.00001)
#print "weightedPR_w_norm", result
elif type == "weightedPR_wo_norm":
result = algo.weighted_PR_wonorm(G, damping_factor = 0.85, max_iterations = 100, min_delta = 0.00001)
#print "weightedPR_wo_norm", result
elif type == "indegree":
result = dp.rank_univ_indegree(G)
#print "indegree", result
else:
print "error type!"
return result
def main():
# bucket = {}
# f = open("../result/result_top50_cs_newdata_apr09/year_statistical_from1995_to2015.csv","r")
# f.readline()
# for line in f:
# lines = line.split(",")
# try:
# bucket.update({lines[0].strip() : int(lines[2].strip())})
# except:
# pass
# f.close()
#
# node_list, edge_list = dp.read_data_in_range("../data/data_may28_new/data_top50_ee.csv",
# "../data/data_may28_new/top50_ee_2015.txt",
# start_year = 1992, end_year = 2013, self_edge = True)
node_list, edge_list = dp.read_data("../data/data_may28_new/data_top50_ee.csv",
"../data/data_may28_new/top50_ee_2015.txt",
self_edge = False, extended = False)
print len(node_list), node_list
print len(edge_list), edge_list
exit(0)
G = dp.construct_graph(node_list, edge_list)
top_50 = []
f = open("../data/data_may28_new/top50_ee_2015.txt","r")
for line in f:
line = line.strip().lower()
top_50.append(line)
f.close()
print len(G.edges())
print len(G.nodes())
nodes = dp.rank_univ(G, t = "in_degree")
f = open("../result/result_may28/ee/comparison/ee_1951-1991_indegree.csv","w")
for node in nodes:
if node[0] in top_50:
f.write("%s;%d\n" %(node[0], node[1]))
f.close()
weighted_pagerank = algo.weighted_PR_wnorm(G, damping_factor = 0.85, max_iterations = 100, min_delta = 0.00001)
result = sorted(weighted_pagerank.iteritems(), key = lambda asd:asd[1], reverse = True)
f = open("../result/result_may28/ee/comparison/ee_1992-2013_weightedPR_w_norm.csv","w")
for r in result:
if r[0] in top_50:
f.write("%s;%.5f\n" %(r[0], r[1]))
f.close()
weighted_pagerank = algo.weighted_PR_wonorm(G, damping_factor = 0.85, max_iterations = 100, min_delta = 0.00001)
s = sum(weighted_pagerank.values())
for rank in weighted_pagerank:
weighted_pagerank[rank] = weighted_pagerank[rank]*50.0/s
result = sorted(weighted_pagerank.iteritems(), key = lambda asd:asd[1], reverse = True)
f = open("../result/result_may28/ee/comparison/ee_1992-2013_weightedPR_wo_norm.csv","w")
for r in result:
if r[0] in top_50:
f.write("%s;%.5f\n" %(r[0], r[1]))
f.close()
#
# hits = algo.HITS(G, max_iterations = 100, min_delta = 0.00001)
# result = sorted(hits.iteritems(), key = lambda asd:asd[1], reverse = True)
# f = open("../result/result_may28/me/extendedGwselfedges/cs_hits.csv","w")
# for r in result:
# if r[0] in top_50:
# f.write("%s;%.5f\n" %(r[0], r[1]))
# f.close()
hits = algo.weighted_HITS(G, max_iterations = 100, min_delta = 0.00001)
result = sorted(hits.iteritems(), key = lambda asd:asd[1], reverse = True)
f = open("../result/result_may28/ee/comparison/ee_1992-2013_hits_weighted.csv","w")
for r in result:
if r[0] in top_50:
f.write("%s;%.5f\n" %(r[0], r[1]))
f.close()
hubavg = algo.hubavg_HITS(G, max_iterations = 100, min_delta = 0.00001)
result = sorted(hubavg.iteritems(), key = lambda asd:asd[1], reverse = True)
f = open("../result/result_may28/ee/comparison/ee_1992-2013_hits_hubavg.csv","w")
for r in result:
if r[0] in top_50:
f.write("%s;%.5f\n" %(r[0], r[1]))
f.close()
# salsa = algo.SALSA(G)
# result = sorted(salsa.iteritems(), key = lambda asd:asd[1], reverse = True)
# f = open("../result/result_top50_cs_newdata_apr09/result_top50_cs/univ_top50_cs_from2000_salsa.csv","w")
# for r in result:
# f.write("%s;%.5f\n" %(r[0], r[1]))
# f.close()
#
# salsa = algo.modified_SALSA(G)
# result = sorted(salsa.iteritems(), key = lambda asd:asd[1], reverse = True)
# f = open("../result/result_top50_cs_extended/entire/univ_top40_me_from1946_to1990_salsa_modified.csv","w")
# for r in result:
# if r[0] in top_50:
# f.write("%s;%.5f\n" %(r[0], r[1]))
# f.close()
#
# credit = algo.CreditPropagation(G, original_rank = hits, cr = 0.8, max_iterations = 10000, min_delta = 0.00001)
# result = sorted(credit.iteritems(), key = lambda asd:asd[1], reverse = True)
# f = open("../result/result_top50_cs_newdata_apr09/result_top50_cs_subtracted_woselfedge/univ_top50_cs_wo_selfedges_CreditProp_hits.csv","w")
# for r in result:
# if r[0] in top_50:
# f.write("%s;%.5f\n" %(r[0], r[1]))
# f.close()
""" new experiments on authavg and weightedHITS_normalized @ May 13th """