PageRank

In this example we create a directed graph and compute PageRank. The graph is the same as in the wikipedia.

Start by importing the package

import jgrapht
import jgrapht.algorithms.scoring as scoring

Creating a graph is done using the factory method. By default graphs are directed and weighted.

g = jgrapht.create_graph()

We can add vertices by providing their identifier. The method returns True if the vertex was added, False otherwise. Identifiers of vertices and edges are always integers.

for v in range(0, 11):
    g.add_vertex(v)

Our graph’s vertex set now looks like

print (g.vertices)

Out:

{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}

We also add the edges

g.add_edges_from([(1, 2), (2, 1), (3, 0), (3, 1), (4, 1), (4, 3), (4, 5), (5, 1), (5, 4),
   (6, 1), (6, 4), (7, 1),  (7, 4), (8, 1),  (8, 4), (9, 4), (10, 4)])

Out:

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]

We now have the following edges

print(g.edges)

Out:

{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}

Execute Pagerank using

pagerank = scoring.pagerank(g)

The returned object is a dictionary from vertices to floating point values. Let us store it in a list

result = [pagerank[v] for v in g.vertices]
print(result)

Out:

[0.03278149315935664, 0.38435780990602514, 0.34295342441583615, 0.03908709209997997, 0.08088569323452037, 0.03908709209997997, 0.016169479016860222, 0.016169479016860222, 0.016169479016860222, 0.016169479016860222, 0.016169479016860222]