# Modeling and Predicting Popularity Dynamics of Microblogs using Self-Excited Hawkes Processes

arXiv: https://arxiv.org/abs/1503.02754 · HTML: https://mlsanity.com/paper/1503.02754/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2015.

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

- [An Invertible Graph Diffusion Neural Network for Source Localization](https://mlsanity.com/paper/2206.09214/) (2022)
- [CCasGNN: Collaborative Cascade Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/2112.03644/) (2021)
- [Explicit Time Embedding Based Cascade Attention Network for Information Popularity Prediction](https://mlsanity.com/paper/2308.09976/) (2023)
- [Incomplete Gamma Integrals for Deep Cascade Prediction using Content, Network, and Exogenous Signals](https://mlsanity.com/paper/2106.07012/) (2021)
- [Learning Structure-enhanced Temporal Point Processes with Gromov-Wasserstein Regularization](https://mlsanity.com/paper/2503.23002/) (2025)

## Abstract (arXiv)

The ability to model and predict the popularity dynamics of individual user generated items on online media has important implications in a wide range of areas. In this paper, we propose a probabilistic model using a Self-Excited Hawkes Process(SEHP) to characterize the process through which individual microblogs gain their popularity. This model explicitly captures the triggering effect of each forwarding, distinguishing itself from the reinforced Poisson process based model where all previous forwardings are simply aggregated as a single triggering effect. We validate the proposed model by applying it on Sina Weibo, the most popular microblogging network in China. Experimental results demonstrate that the SEHP model consistently outperforms the model based on reinforced Poisson process.