3.3 文章影响力评价问题

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import numpy as np

from scipy.sparse.linalg import eigs

import pylab as plt

  

w = np.array([[0, 1, 0, 1, 1, 1],
              [0, 0, 0, 1, 1, 1],
              [1, 1, 0, 1, 0, 0],
              [0, 0, 0, 0, 1, 1],
              [0, 0, 1, 0, 0, 1],
              [0, 0, 1, 0, 0, 0]])

r = np.sum(w,axis=1,keepdims=True)

n = w.shape[0]

d = 0.85

P = (1-d)/n+d*w/r #利用矩阵广播

w,v = eigs(P.T,1) #求最大特征值及对应的特征向量

v = v/sum(v)

v = v.real

print("最大特征值为：",w.real)

print("归一化特征向量为：\n",np.round(v,4))

plt.bar(range(1,n+1),v.flatten(),width=0.6)

plt.show()


print("学号：3004")