def RGG(n, beta, mean_degree):
G = nx.empty_graph(n)
powerLawArray = utils.powerLawArray(n, beta, mean_degree)
powerLawDegreeArray = np.array(powerLawArray, dtype = np.longlong)
sumOfDegrees = powerLawDegreeArray.sum()
delimiterArray = np.cumsum(powerLawDegreeArray)
delimiterArray = np.insert(delimiterArray, 0, 0)
delimiterArray = np.delete(delimiterArray, n)
someCounter = 0
while someCounter < sumOfDegrees/2:
G.add_edge(np.searchsorted(delimiterArray, rnd.randrange(sumOfDegrees)),
np.searchsorted(delimiterArray, rnd.randrange(sumOfDegrees)))
someCounter += 1
txtname = "generated/adj-%s-%s-%s-.txt" % (str(n), str(beta), str(mean_degree))
nx.write_adjlist(G, txtname)
degreeSequence=sorted(nx.degree(G).values(),reverse=True)
dmax=max(degreeSequence)
plt.clf()
plt.cla()
plt.loglog(degreeSequence,'b-',marker='o')
plt.title("Degree rank plot")
plt.ylabel("degree")
plt.xlabel("rank")
if n < 1000:
plt.axes([0.45,0.45,0.45,0.45])
plt.cla()
Gcc=nx.connected_component_subgraphs(G)[0]
pos=nx.spring_layout(Gcc)
plt.axis('off')
nx.draw_networkx_nodes(Gcc,pos,node_size=20)
nx.draw_networkx_edges(Gcc,pos,alpha=0.4)
pngname = "generated/graph-%s-%s-%s-.png" % (str(n), str(beta), str(mean_degree))
plt.savefig(pngname)
def RGG(n, beta, mean_degree):
G = nx.empty_graph(n)
powerLawArray = utils.powerLawArray(n, beta, mean_degree)
powerLawDegreeArray = np.array(powerLawArray, dtype = np.longlong)
sumOfDegrees = powerLawDegreeArray.sum()
delimiterArray = np.cumsum(powerLawDegreeArray)
delimiterArray = np.insert(delimiterArray, 0, 0)
delimiterArray = np.delete(delimiterArray, n)
someCounter = 0
while someCounter < sumOfDegrees/2:
G.add_edge(np.searchsorted(delimiterArray, rnd.randrange(sumOfDegrees)),
np.searchsorted(delimiterArray, rnd.randrange(sumOfDegrees)))
someCounter += 1
txtname = "test/adj-%s-%s-%s-.txt" % (str(n), str(beta), str(mean_degree))
nx.write_adjlist(G, txtname)
# number of vertices n = 10000 # create an empty graph on n vertices G = nx.empty_graph(n) # power of the model beta = 1.7 # average degree of the graph mean_degree = 50 # generate a power-law array with defined parameters powerLawArray = utils.powerLawArray(n, beta, mean_degree) print np.max(powerLawArray) #может просто последний элемент печатать? впрочем, неважно # as stated in Chung-Lu article we create an array of ints just taking the lower bound of the values # from the power-law sequence #powerLawDegreeSequence = [0]*n powerLawDegreeArray = np.array(powerLawArray, dtype = np.longlong) print powerLawDegreeArray.size # counter. sumOfDegrees = Sum_i d_i sumOfDegrees = powerLawDegreeArray.sum() print sumOfDegrees
