BagIt-Profile-Identifier: https://raw.githubusercontent.com/UTS-eResearch/datacrate/master/spec/0.3/profile-datacrate-v0.3.json
DataCrate-Specification-Identifier: https://github.com/UTS-eResearch/datacrate/blob/master/spec/0.3/data_crate_specification_v0.3.md
External-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.
Bagging-Date: 2018-09-25T23:17:59.203Z