A machine-readable version of this page, created at 2018-09-25T23:17:59.203Z is available CATALOG.json
@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? | |
path? | data |
contact? | J. Xuan |
citation? | Topic Model for Graph Mining |
hasPart? | |
identifier? | doi.org/10.4225/59/59e3d6d08faa9 |
license? | GPL 3 |
publisher? | University of Technology Sydney |
distribution? | https://data.research.uts.edu.au/examples/v0.3/GTM.zip |
This file was created at 2018-09-25T23:18:00.502Z by Calcyte which implements the Draft DataCrate Packaging format, version 0.3