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February 8, 2017 00:12
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Does google's pagerank algorithm on a directed graph. Requires networkx for the graphs.
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try: | |
import networkx as nx | |
except ImportError: | |
raise ImportError('Requires networkx') | |
def pageranker(g, damp, iterations): | |
#Does this for a set amount of iterations. | |
#In the future, may add convergence conditions. | |
#Not optimal for large graphs. Should probably store the connected_to list somewhere instead of calling it each time. | |
dict_of_ranks = {} | |
prior_values_of_rank = {} | |
for i in g.nodes(): | |
dict_of_ranks[i] = 1 | |
prior_values_of_rank[i] = 1 | |
for k in range(iterations): | |
for i in g.nodes(): | |
pagerank_of_node = 0 | |
#PR(x) = (1-d) + d*(PR(T1)/C(T1) + ... + PR(TN)/C(TN)) for T1,...,TN connected to x. | |
#PR = pagerank | |
#C(Ti) = outdegree of Ti | |
if len(connected_to(g, i)) == 0: | |
pagerank_of_node = (1-damp) | |
else: | |
for j in connected_to(g, i): | |
pagerank_of_node += (dict_of_ranks[j]/g.out_degree(j)) | |
pagerank_of_node = pagerank_of_node * damp | |
pagerank_of_node += (1-damp) | |
prior_values_of_rank[i] = pagerank_of_node | |
for i in prior_values_of_rank: | |
dict_of_ranks[i] = prior_values_of_rank[i] | |
for i in dict_of_ranks: | |
dict_of_ranks[i] = dict_of_ranks[i]/len(g.nodes()) | |
return dict_of_ranks | |
def connected_to(g, node): | |
list_of_connected = [] | |
for i in g.nodes(): | |
if node in g[i]: | |
list_of_connected.append(i) | |
return list_of_connected |
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Note this gives an estimate, it does not give an accurate value. Leaving room to further update to make it more accurate.