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PLANE

PLANE: Probabilistic Latent Document Network Embedding

INTRODUCTION

This is an implementation of PLANE - a method for embedding a document network in a low dimensional space from Le & Lauw (ICDM 2014).

Usage:

perl plane.pl		--num_topics $num_topics
			--dim $dim
			--alpha $alpha
			--beta $beta
			--gamma $gamma
			--EM_iter $EM_iter
			--Quasi_iter $Quasi_iter
			--data $data
			--graph $graph
			--output_file $output_file

Arguments:

$num_topics: number of topics
$dim: number of dimensions (default 2)
$alpha: Dirichlet parameter (default 0.01)
$beta: covariance for Gaussian prior of topic coordinates (default 0.1*$num_docs)
$gamma: covariance for Gaussian prior of document coordinates (default 0.1*$num_topics)
$EM_iter: number of iterations for EM (default 100)
$Quasi_iter: maximum iterations of Quasi-Newton (default 10)
$data: input data
$graph: document network
$output_file: output file

Details:

  • This implementation needs Algorithm::LBFGS library for quasi-Newton method L-BFGS. The library can be downloaded at http://search.cpan.org/~laye/Algorithm-LBFGS-0.16/lib/Algorithm/LBFGS.pm. To install,

    cpan Algorithm::LBFGS
    
  • Example of input data with 3 documents (numbers are ids of words):

    0 1 1 2 2 3 4 4 5 6 7 7 8 8 8 8 9 10 11 12 13 13 14 14 15 15 15 16
    17 18 19 20 20 21 22 23 24 25 25 25 25 25 25 26 27 27 28 29
    30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 50 51 52 53 54 54 55 56 57 58

  • Document network is represented by a matrix A: NxN. N is the number of documents and A[i,j]=A[j,i]=1 when there is an edge connecting documents i and j.

    For example,

    0 1 0
    1 0 1
    0 1 0

HOW TO CITE

If you use PLANE for your research, please cite:

@inproceedings{plane,
    title={Probabilistic Latent Document Network Embedding},
    author={Le, Tuan MV and Lauw, Hady W},
    booktitle={IEEE International Conference on Data Mining},
    year={2014}
}

The paper can be downloaded from: http://www.hadylauw.com/publications/icdm14.pdf

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