A machine-readable version of this page, created at 2019-01-30T00:34:03.335Z is available CATALOG.json

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@id	https://doi.org/10.4225/59/59e3d6d08faa9
name?	GTM
@type	Dataset?
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 (Type: Person) J. Lu (Type: Person) G. Zhang (Type: Person) X. Luo (Type: Person)
path?	data
contact?	J. Xuan (Type: Person)
citation?	Topic Model for Graph Mining (Type: ScholarlyArticle)
hasPart?	gtm_gs.m (Type: File) LICENSE (Type: File) README.txt (Type: File) test_gtm.m (Type: File) GTM (Type: Dataset)
identifier?	doi.org/10.4225/59/59e3d6d08faa9
license?	GPL 3 (Type: Thing)
publisher?	University of Technology Sydney
distribution?	https://data.research.uts.edu.au/examples/v1.0/GTM.zip (Type: DataDownload)

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