def evaluation(graph): print '---------------------------------' histogram = nx.classes.function.degree_histogram(graph) print '########## Histogram ############' for his in histogram: print str(his) + " ", print print "Cluster_Coeff: ", tl.avg_cluster(graph) print "AVG_Shortest_Path: ", nx.algorithms.shortest_paths.generic.average_shortest_path_length(graph) # print "Assortativity: ", nx.algorithms.mixing.degree_assortativity(graph) print "Giant_Component: ", tl.giant_CC(graph) print "Diameter: ", nx.algorithms.distance_measures.diameter(graph) print "Densification: ", str(nx.number_of_nodes(graph)) + " " + str(nx.number_of_edges(graph)) print '--------------------------------' print
def evaluation(graph): print '---------------------------------' histogram = nx.classes.function.degree_histogram(graph) print '########## Histogram ############' for his in histogram: print str(his) + " ", print print "Cluster_Coeff: ", tl.avg_cluster(graph) print "AVG_Shortest_Path: ", nx.algorithms.shortest_paths.generic.average_shortest_path_length( graph) # print "Assortativity: ", nx.algorithms.mixing.degree_assortativity(graph) print "Giant_Component: ", tl.giant_CC(graph) print "Diameter: ", nx.algorithms.distance_measures.diameter( graph) print "Densification: ", str(nx.number_of_nodes(graph)) + " " + str( nx.number_of_edges(graph)) print '--------------------------------' print
import networkx as nx import hw1_tools as tl import sys graph = nx.read_adjlist(sys.argv[1]) histogram = nx.classes.function.degree_histogram(graph) print histogram print "Cluster_Coeff: ", tl.avg_cluster(graph) print "AVG_Shortest_Path: ", nx.algorithms.shortest_paths.generic.average_shortest_path_length(graph) print "Assortativity: ", nx.algorithms.mixing.degree_assortativity(graph) print "Giant_Component: ", tl.giant_CC(graph) print "Diameter: ", nx.algorithms.distance_measures.diameter(graph)