@id	data/GTM
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.
isPartOf	GTM (Type: Dataset)
datePublished^?	2018-03-10
creator^?	J. Xuan (Type: Person) J. Lu (Type: Person) G. Zhang (Type: Person) X. Luo (Type: Person)
path^?	GTM
contact^?	J. Xuan (Type: Person)
citation^?	Topic Model for Graph Mining (Type: ScholarlyArticle)
hasPart^?	GTM/gtm_gs.m (Type: File) GTM/LICENSE (Type: File) GTM/README.txt (Type: File) GTM/test_gtm.m (Type: File) data/GTM/CATALOG (Type: Dataset)
identifier^?	./GTM
license^?	GPL 3 (Type: Thing)
publisher^?	University of Technology Sydney

This file was created at 2019-01-30T00:34:03.988Z by Calcyte which implements the Draft DataCrate Packaging format, version 1.0