WeightedPageRank
Related Doc:
package examples
one iteration of pagerank inputPagerank: <'src_id_input, 'mass_input> return <'src_id, 'mass_n, 'mass_input>
one iteration of pagerank inputPagerank: <'src_id_input, 'mass_input> return <'src_id, 'mass_n, 'mass_input>
Here is a highlevel view of the unweighted algorithm: let N: number of nodes inputPagerank(N_i): prob of walking to node i, d(N_j): N_j's out degree then pagerankNext(N_i) = (\sum_{j points to i} inputPagerank(N_j) / d_j) deadPagerank = (1 - \sum_{i} pagerankNext(N_i)) / N randomPagerank(N_i) = userMass(N_i) * ALPHA + deadPagerank * (1-ALPHA) pagerankOutput(N_i) = randomPagerank(N_i) + pagerankNext(N_i) * (1-ALPHA)
For weighted algorithm: let w(N_j, N_i): weight from N_j to N_i tw(N_j): N_j's total out weights then pagerankNext(N_i) = (\sum_{j points to i} inputPagerank(N_j) * w(N_j, N_i) / tw(N_j))
read the pregenerated nodes file <'src_id, 'dst_ids, 'weights, 'mass_prior>
the total number of nodes, single line file
test convergence, if not yet, kick off the next iteration
test convergence, if not yet, kick off the next iteration
weighted page rank for the given graph, start from the given pagerank, perform one iteartion, test for convergence, if not yet, clone itself and start the next page rank job with updated pagerank as input.
This class is very similar to the PageRank class, main differences are: 1. supported weighted pagerank 2. the reset pagerank is pregenerated, possibly through a previous job 3. dead pagerank is evenly distributed
Options: --pwd: working directory, will read/generate the following files there numnodes: total number of nodes nodes: nodes file <'src_id, 'dst_ids, 'weights, 'mass_prior> pagerank: the page rank file eg pagerank_0, pagerank_1 etc totaldiff: the current max pagerank delta Optional arguments: --weighted: do weighted pagerank, default false --curiteration: what is the current iteration, default 0 --maxiterations: how many iterations to run. Default is 20 --jumpprob: probability of a random jump, default is 0.1 --threshold: total difference before finishing early, default 0.001