# Powered Hawkes-Dirichlet Process: Challenging Textual Clustering using a Flexible Temporal Prior

arXiv: https://arxiv.org/abs/2109.07170 · HTML: https://mlsanity.com/paper/2109.07170/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering](https://mlsanity.com/paper/1701.09177/) (2017)
- [Interactions in information spread: quantification and interpretation using stochastic block models](https://mlsanity.com/paper/2004.04552/) (2020)
- [Powered Dirichlet Process for Controlling the Importance of "Rich-Get-Richer" Prior Assumptions in Bayesian Clustering](https://mlsanity.com/paper/2104.12485/) (2021)
- [Lifespan and propagation of information in On-line Social Networks a Case Study](https://mlsanity.com/paper/1403.1486/) (2014)

## Built on by (most cited first, 5 of 5)

- [Properties of Reddit News Topical Interactions](https://mlsanity.com/paper/2209.07816/) (2022)
- [Multivariate Powered Dirichlet Hawkes Process](https://mlsanity.com/paper/2212.05995/) (2022)
- [Le Processus Powered Dirichlet-Hawkes comme A Priori Flexible pour Clustering Temporel de Textes](https://mlsanity.com/paper/2201.12568/) (2022)
- [Interactions in Information Spread](https://mlsanity.com/paper/2209.08026/) (2022)
- [Dirichlet-Survival Process: Scalable Inference of Topic-Dependent Diffusion Networks](https://mlsanity.com/paper/2212.05996/) (2022)

## Abstract (arXiv)

The textual content of a document and its publication date are intertwined. For example, the publication of a news article on a topic is influenced by previous publications on similar issues, according to underlying temporal dynamics. However, it can be challenging to retrieve meaningful information when textual information conveys little information or when temporal dynamics are hard to unveil. Furthermore, the textual content of a document is not always linked to its temporal dynamics. We develop a flexible method to create clusters of textual documents according to both their content and publication time, the Powered Dirichlet-Hawkes process (PDHP). We show PDHP yields significantly better results than state-of-the-art models when temporal information or textual content is weakly informative. The PDHP also alleviates the hypothesis that textual content and temporal dynamics are always perfectly correlated. PDHP allows retrieving textual clusters, temporal clusters, or a mixture of both with high accuracy when they are not. We demonstrate that PDHP generalizes previous work --such as the Dirichlet-Hawkes process (DHP) and Uniform process (UP). Finally, we illustrate the changes induced by PDHP over DHP and UP in a real-world application using Reddit data.