Data Set

GTM

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.

GPL 3

GPL 3

GPL 3

GPL 3

J. Xuan

J

Xuan

Person

University of Technology Sydney

J. Lu

J

Luo

Person

University of Technology Sydney

G. Zhang

G

Zhang

Person

University of Technology Sydney

X. Luo

X

Luo

Person

University of Technology Sydney

University of Technology Sydney

GPL 3

GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007

10.1109/TCYB.2014.2386282

Topic Model for Graph Mining

2168-2267

IEEE Transactions on Cybernetics

2015

@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},
number={12},
pages={2792-2803},
keywords={data mining;data structures;graph theory;pattern classification;text analysis;unsupervised learning;Bernoulli distribution;GTM;bag-of-word assumption;classification;edge modeling;graph dataset;graph mining;graph nodes;graph representation;graph supervised learning;graph-structured data;innovative graph topic model;latent Dirichlet allocation;latent topic discovery;unsupervised learning;Chemical elements;Chemicals;Data mining;Data models;Hidden Markov models;Inference algorithms;Vectors;Graph mining;latent Dirichlet allocation (LDA);topic model},
doi={10.1109/TCYB.2014.2386282},
ISSN={2168-2267},
month={Dec},}

ScholarlyArticle