CMSC 641 Project
UMBC CMSC 641 Project - PageRank, HITS, and their Monte Carlo variants
Contributors: Rose Kunnappallil, Erfan Noury
Usage
usage: main.py [-h] -a {pagerank,hits,mcpagerank,mchits} [--random-data]
[--teleporting-prob TELEPORTING_PROB] [--sparsity SPARSITY]
[-n NUM_NODES] [--num-iter NUM_ITER] [--iterative] [--verbose]
Calculate importance of webpages using PageRank, HITS, and their Monte Carlo
variants.
optional arguments:
-h, --help show this help message and exit
-a {pagerank,hits,mcpagerank,mchits}, --algorithm {pagerank,hits,mcpagerank,mchits}
Select which algorithm to use
--random-data Use random data to test the algorithms
--teleporting-prob TELEPORTING_PROB
Teleporting probability for the PageRank algorithm
--sparsity SPARSITY Sparsity of the random graph (probability of an edge
being present between two nodes
-n NUM_NODES, --num-nodes NUM_NODES
Graph size (number of nodes)
--num-iter NUM_ITER Number of iterations to run the algorithm
--iterative Whether to use the Power Iteration
--verbose Increase verbosity to show running time
Examples
To run PageRank algorithm on a random graph with 2000 nodes and report the running time, use
$ python main.py -a pagerank --random-data -n 2000 --verbose
To run the Iterative HITS algorithm on a random graph with 15000 nodes, with sparsity of 0.1, use
$ python main.py -a hits --random-data -n 15000 --sparsity 0.1 --num-iter 20 --iterative