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)
