def sensitive():
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/sensitivity_weightedPR_wo_norm_1995-2015+mit1.csv",
"w")
node_list, edge_list = dp.read_data_in_range(
"../data/data_top50_cs_apr09.csv",
start_year=1995,
end_year=2015,
self_edge=False)
G = dp.construct_graph(node_list, edge_list)
hits = algo.weighted_PR_wonorm(G,
damping_factor=0.85,
max_iterations=100,
min_delta=0.00001)
result = sorted(hits.iteritems(), key=lambda asd: asd[1], reverse=True)
G.clear()
original_r = []
for e in result:
if e[0] in top_50:
original_r.append(e[0])
fo.write("origin,")
for node in original_r:
fo.write("%s," % node)
fo.write("\n")
for node in top_50:
if not node == "mit":
node_list, edge_list = dp.read_data_in_range(
"../data/data_top50_cs_apr09.csv",
start_year=1995,
end_year=2015,
self_edge=False)
G = dp.construct_graph(node_list, edge_list)
G = add_non_existing_edges(
G, node, "mit", weight=1) ### add one edge from MIT to <node>
hits = algo.weighted_PR_wonorm(G,
damping_factor=0.85,
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])
fo.write("%s," % node)
for r in res1:
fo.write("%s," % r)
fo.write("\n")
fo.close()
def sensitive_2():
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/mit+1/sensitivity_diff_CreditProp_hits_1995-2015+mit1.csv","w")
node_list, edge_list = dp.read_data_in_range("../data/data_top50_cs_apr09.csv", start_year = 1995, end_year = 2015, self_edge = False)
G = dp.construct_graph(node_list, edge_list)
hits = algo.HITS(G, max_iterations = 100, min_delta = 0.00001)
hits = algo.CreditPropagation(G, original_rank = hits, cr = 0.85, max_iterations = 100, min_delta = 0.00001)
result = sorted(hits.iteritems(), key = lambda asd:asd[1], reverse = True)
G.clear()
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_in_range("../data/data_top50_cs_apr09.csv", start_year = 1995, end_year = 2015, self_edge = False)
G = dp.construct_graph(node_list, edge_list)
G = add_non_existing_edges(G, original_r[k][0], "mit", weight = 1) ### add one edge from MIT to <node>
hits = algo.HITS(G, max_iterations = 100, min_delta = 0.00001)
hits = algo.CreditPropagation(G, original_rank = hits, cr = 0.85, 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():
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/sensitivity_weightedPR_wo_norm_1995-2015+mit1.csv","w")
node_list, edge_list = dp.read_data_in_range("../data/data_top50_cs_apr09.csv", start_year = 1995, end_year = 2015, self_edge = False)
G = dp.construct_graph(node_list, edge_list)
hits = algo.weighted_PR_wonorm(G, damping_factor = 0.85, max_iterations = 100, min_delta = 0.00001)
result = sorted(hits.iteritems(), key = lambda asd:asd[1], reverse = True)
G.clear()
original_r = []
for e in result:
if e[0] in top_50:
original_r.append(e[0])
fo.write("origin,")
for node in original_r:
fo.write("%s," %node)
fo.write("\n")
for node in top_50:
if not node == "mit":
node_list, edge_list = dp.read_data_in_range("../data/data_top50_cs_apr09.csv", start_year = 1995, end_year = 2015, self_edge = False)
G = dp.construct_graph(node_list, edge_list)
G = add_non_existing_edges(G, node, "mit", weight = 1) ### add one edge from MIT to <node>
hits = algo.weighted_PR_wonorm(G, damping_factor = 0.85, 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])
fo.write("%s," %node)
for r in res1:
fo.write("%s," %r)
fo.write("\n")
fo.close()
def sensitive_2():
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/mit+1/sensitivity_diff_CreditProp_hits_1995-2015+mit1.csv",
"w")
node_list, edge_list = dp.read_data_in_range(
"../data/data_top50_cs_apr09.csv",
start_year=1995,
end_year=2015,
self_edge=False)
G = dp.construct_graph(node_list, edge_list)
hits = algo.HITS(G, max_iterations=100, min_delta=0.00001)
hits = algo.CreditPropagation(G,
original_rank=hits,
cr=0.85,
max_iterations=100,
min_delta=0.00001)
result = sorted(hits.iteritems(), key=lambda asd: asd[1], reverse=True)
G.clear()
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_in_range(
"../data/data_top50_cs_apr09.csv",
start_year=1995,
end_year=2015,
self_edge=False)
G = dp.construct_graph(node_list, edge_list)
G = add_non_existing_edges(
G, original_r[k][0], "mit",
weight=1) ### add one edge from MIT to <node>
hits = algo.HITS(G, max_iterations=100, min_delta=0.00001)
hits = algo.CreditPropagation(G,
original_rank=hits,
cr=0.85,
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()