Relational Topic Models for Document Networks
Abstract
We develop the relational topic model (RTM), a model of documents and the links between them. For each pair of documents, the RTM models their link as a binary random variable that is conditioned on their contents. The model can be used to summarize a network of documents, predict links between them, and predict words within them. We derive efficient inference and learning algorithms based on variational methods and evaluate the predictive performance of the RTM for large networks of scientific abstracts and web documents.
Cite
Text
Chang and Blei. "Relational Topic Models for Document Networks." Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009.Markdown
[Chang and Blei. "Relational Topic Models for Document Networks." Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009.](https://mlanthology.org/aistats/2009/chang2009aistats-relational/)BibTeX
@inproceedings{chang2009aistats-relational,
title = {{Relational Topic Models for Document Networks}},
author = {Chang, Jonathan and Blei, David},
booktitle = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics},
year = {2009},
pages = {81-88},
volume = {5},
url = {https://mlanthology.org/aistats/2009/chang2009aistats-relational/}
}