# Implement r_tplus1 = beta * M * r_t + (1 - beta) / N def page_rank_one_iteration(adjacency_matrix, beta, r_t):

r_tplus1 = None ### FILL YOUR CODE ###

######

return np.round(r_tplus1, 2)

### FILL YOUR CODE ### # The adjacency matrix A from Q1(e) A= # The number of unique users N= ######

beta = 0.8 r_0 = np.full((N, 1), 1 / N)

r_1 = page_rank_one_iteration(A, beta, r_0) assert r_1.shape == (N, 1) r_1

CSV:

from to 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 10 0 11 0 12 0 13 0 17 0 19 0 21 0 31 1 2 1 3 1 7 1 13 1 17 1 19 1 21 1 30 2 3 2 7 2 8 2 9 2 13 2 27 2 28 2 32 3 7 3 12 3 13 4 6 4 10 5 6 5 10 5 16 6 16 8 30 8 32 8 33 9 33 13 33 14 32 14 33 15 32 15 33 18 32 18 33 19 33 20 32 20 33 22 32 22 33 23 25 23 27 23 29 23 32 23 33 24 25 24 27 24 31 25 31 26 29 26 33 27 33 28 31 28 33 29 32 29 33 30 32 30 33 31 32 31 33 32 33

Need solution to compare answer. Pandas, numpy.

Implement the page rank algorithm (in Figure 1), which was originally used for Google Search, by filling in the body of the below function (in Figure 2), capturing one single iteration of the page rank algorithm. rt+1=betaMrt+N1beta rt and rt+1 : Two column vectors have the shape of (N,1) and store the popular value of every user at the iteration t and t+1 correspondingly. In the page rank algorithm, a person who knows a popular person is also popular. N : The number of unique users M : The matrix has the shape of (N,N). - Mij=dj1 if the user with the ID =i knows the user with the ID =j. Otherwise, Mij=0. - dj is the number of users whom the user indexed by j knows. - iMij=1 Note that the between Mrt is a matrix multiplication. Figure 1: Page rank algorithm Figure 2: Code to be completed for Q1(f)