def node_ranking(filename='network.csv', alpha=0.95):
'''
Rank the nodes in the network imported from a CSV file.
(1) import the adjacency matrix from `filename` file.
(2) compute pagerank scores of all the nodes
(3) return a list of node IDs sorted by descending order of pagerank scores
Input:
filename: the csv filename for the adjacency matrix, a string.
alpha: a float scalar value, which is the probability of choosing option 1 (randomly follow a link on the node)
Output:
sorted_ids: the list of node IDs (starting from 0) in descending order of their pagerank scores, a python list of integer values, such as [2,0,1,3].
Hint: you could solve this problem using 3 lines of code.
'''
#########################################
## INSERT YOUR CODE HERE
# import the adjacency matrix A from csv file "filename"
A = import_A(filename)
# compute the pagerank scores of all the nodes using PageRank algorithm implemented in problem 5
x = pagerank(A)
# compute the sorted list of node IDs, higher ranked nodes should have higher pagerank scores.
sorted_ids = score2rank(x)
#########################################
return sorted_ids
def node_ranking(filename='network.csv', alpha=0.95):
'''
Rank the nodes in the network imported from a CSV file.
(1) import the adjacency matrix from `filename` file.
(2) compute pagerank scores of all the nodes
(3) return a list of node IDs sorted by descending order of pagerank scores
Input:
filename: the csv filename for the adjacency matrix, a string.
alpha: a float scalar value, which is the probability of choosing option 1 (randomly follow a link on the node)
Output:
sorted_ids: the list of node IDs (starting from 0) in descending order of their pagerank scores, a python list of integer values, such as [2,0,1,3].
'''
A = import_A(filename)
x = pagerank(A, alpha)
sorted_ids = score2rank(x)
return sorted_ids