# Multivariate Powered Dirichlet Hawkes Process

arXiv: https://arxiv.org/abs/2212.05995 · HTML: https://mlsanity.com/paper/2212.05995/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 12 Dec 2022.

## Builds on (most cited first, 7 of 7)

- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Modeling Information Propagation with Survival Theory](https://mlsanity.com/paper/1305.3616/) (2013)
- [Powered Dirichlet Process for Controlling the Importance of "Rich-Get-Richer" Prior Assumptions in Bayesian Clustering](https://mlsanity.com/paper/2104.12485/) (2021)
- [Powered Hawkes-Dirichlet Process: Challenging Textual Clustering using a Flexible Temporal Prior](https://mlsanity.com/paper/2109.07170/) (2021)
- [Lifespan and propagation of information in On-line Social Networks a Case Study](https://mlsanity.com/paper/1403.1486/) (2014)
- [Correlated Cascades: Compete or Cooperate](https://mlsanity.com/paper/1510.00936/) (2015)
- [Properties of Reddit News Topical Interactions](https://mlsanity.com/paper/2209.07816/) (2022)

## Built on by (most cited first, 1 of 1)

- [Uncovering Social Network Activity Using Joint User and Topic Interaction](https://mlsanity.com/paper/2506.12842/) (2025)

## Abstract (arXiv)

The publication time of a document carries a relevant information about its semantic content. The Dirichlet-Hawkes process has been proposed to jointly model textual information and publication dynamics. This approach has been used with success in several recent works, and extended to tackle specific challenging problems --typically for short texts or entangled publication dynamics. However, the prior in its current form does not allow for complex publication dynamics. In particular, inferred topics are independent from each other --a publication about finance is assumed to have no influence on publications about politics, for instance. In this work, we develop the Multivariate Powered Dirichlet-Hawkes Process (MPDHP), that alleviates this assumption. Publications about various topics can now influence each other. We detail and overcome the technical challenges that arise from considering interacting topics. We conduct a systematic evaluation of MPDHP on a range of synthetic datasets to define its application domain and limitations. Finally, we develop a use case of the MPDHP on Reddit data. At the end of this article, the interested reader will know how and when to use MPDHP, and when not to.