def main():
citation_graph = load_graph(CITATION_URL)
print compute_in_degrees(citation_graph)
start_time = timeit.default_timer()
dist = in_degree_distribution(citation_graph)
print 'dist =', dist
total = sum(dist.itervalues())
normalized = {key: float(value)/total for key, value in dist.items()}
print(timeit.default_timer() - start_time)
x = normalized.keys()
y = normalized.values()
print len(y)
plt.loglog(x, y, 'ro')
plt.yscale('log')
plt.xscale('log')
plt.minorticks_off()
plt.xlabel('In-degree distribution')
plt.ylabel('Normalized In-degree distribution')
plt.title('Graph of Citations')
plt.grid(True)
plt.savefig('citations-q1.png')
plt.show()
graph = make_complete_graph(m)
print graph
trial = DPATrial(m)
for i in range(m, n):
neighbors = trial.run_trial(m)
graph[i] = neighbors
return graph
graph = dpa(28000, 13)
#out_degree = [len(x) for x in graph.itervalues()]
#print 'out degree: ', out_degree
#print sum(out_degree)/len(out_degree)
dist = in_degree_distribution(graph)
print 'dist: ', dist
total = sum(dist.itervalues())
normalized = {key: float(value)/total for key, value in dist.items()}
#print(timeit.default_timer() - start_time)
x = normalized.keys()
y = normalized.values()
print len(y)
plt.loglog(x, y, 'ro')
plt.yscale('log')
plt.xscale('log')
plt.minorticks_off()
plt.xlabel('In-degree distribution')
