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Compute PageRank on large graphs with off-the-shelf hardware.

Project description

danker - Compute PageRank on large graphs with off-the-shelf hardware.

  • Standalone with any input graph:

    $ pip install danker
    $ python -m danker -h
      usage: python -m danker [-h]
                              left_sorted [right_sorted] damping iterations
                              start_value
    
      danker - Compute PageRank on large graphs with off-the-shelf hardware.
    
      positional arguments:
        left_sorted   A two-column, tab-separated file sorted by the left column.
        right_sorted  The same file as left_sorted but sorted by the right column.
        damping       PageRank damping factor(between 0 and 1).
        iterations    Number of PageRank iterations (>0).
        start_value   PageRank starting value(>0).
    
      optional arguments:
        -h, --help    show this help message and exit
    
    $ wget https://raw.githubusercontent.com/athalhammer/danker/master/test/graphs/test.links
    $ python -m danker test.links 0.85 30 1
       1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.
       Computation of PageRank on 'test.links' with danker took 0.00 seconds.
       C	3.1898535044738043
       B	3.5572213415705725
       A	0.30410528185694391
       D	0.36260066319290651
       F	0.36260066319290651
       E	0.75035528185694389
       G	0.15000000000000002
       H	0.15000000000000002
       I	0.15000000000000002
       K	0.15000000000000002
       L	0.15000000000000002
    
  • As Python library for computing PageRank on large graphs:

    $ pip install danker
    $ python
    >>> import danker
    

    More information on this option can be found at https://danker.rtfd.org.

More information on the project: Compute PageRank on the Wikipedia graph

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