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()