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 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 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 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 """