Cite this work:

Xuan, J; J. Lu ; Zhang, G; Luo, X (2018) GTM. University of Technology Sydney. Datacrate. https://doi.org/10.4225/59/59e3d6d08faa9

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@type Dataset
name GTM
description This demo is the sampling inference for Graph Topic Model, and more details about this model can be found in the following reference: @ARTICLE{7015568, author={J. Xuan and J. Lu and G. Zhang and X. Luo}, journal={IEEE Transactions on Cybernetics}, title={Topic Model for Graph Mining}, year={2015}, volume={45}, A Markov chain Monte Carlo (MCMC) algorithm is developed and implemented to inference the Graph Topic Model (GTM). GTM is a probabilistic graphical model for the data represented by graph structure, e.g., chemical formulas or documents.
datePublished 2018-03-10
creator J. Xuan , J. Lu , G. Zhang , X. Luo
path data
contact J. Xuan
hasPart gtm_gs.m , LICENSE , README.txt , test_gtm.m
license GPL 3
publisher University of Technology Sydney
relatedLink Topic Model for Graph Mining

Files:


@type File
path gtm_gs.m
encodingFormat Plain Text File
contentSize 12656
license GPL 3

@type File
path LICENSE
encodingFormat Plain Text File
contentSize 35049
license GPL 3

@type File
path README.txt
encodingFormat Plain Text File
contentSize 984
license GPL 3

@type File
path test_gtm.m
encodingFormat Plain Text File
contentSize 527
license GPL 3

Contextual info: Person


@type Person
name J. Xuan
affiliation University of Technology Sydney
familyName Xuan
givenName J

@type Person
name J. Lu
affiliation University of Technology Sydney
familyName Lu
givenName J

@type Person
name G. Zhang
affiliation University of Technology Sydney
familyName Zhang
givenName G

@type Person
name X. Luo
affiliation University of Technology Sydney
familyName Luo
givenName X

Contextual info: ScholarlyArticle


@type ScholarlyArticle
name Topic Model for Graph Mining
datePublished 2015
creator J. Xuan , J. Lu , G. Zhang , X. Luo
journal IEEE Transactions on Cybernetics
issn 2168-2267