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 = {0: set([])}
if num_nodes > 0:
for node in range(num_nodes):
edges = []
for head in range(num_nodes):
if node != head:
a = random.random()
if a < probability:
edges.append(head)
graph[node] = set(edges)
return graph
graph = er_graph(2777, 0.05)
print graph
degrees = compute_in_degrees(graph)
print degrees
dist = in_degree_distribution(graph)
print dist
total = sum(dist.itervalues())
normalized = {key: float(value)/total for key, value in dist.items()}
#print(timeit.default_timer() - start_time)
print sum(normalized.itervalues())
#print dist
print normalized
x = normalized.keys()
y = normalized.values()
