# Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes

arXiv: https://arxiv.org/abs/1401.0778 · HTML: https://mlsanity.com/paper/1401.0778/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2014.

## Built on by (most cited first, 20 of 20)

- [Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes](https://mlsanity.com/paper/2105.11152/) (2021)
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- [CCasGNN: Collaborative Cascade Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/2112.03644/) (2021)
- [Graph Representation Learning for Popularity Prediction Problem: A Survey](https://mlsanity.com/paper/2203.07632/) (2022)
- [Explicit Time Embedding Based Cascade Attention Network for Information Popularity Prediction](https://mlsanity.com/paper/2308.09976/) (2023)
- [Survey on Modeling Intensity Function of Hawkes Process Using Neural Models](https://mlsanity.com/paper/2104.11092/) (2021)
- [Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks](https://mlsanity.com/paper/2105.08291/) (2021)
- [Incomplete Gamma Integrals for Deep Cascade Prediction using Content, Network, and Exogenous Signals](https://mlsanity.com/paper/2106.07012/) (2021)
- [Revealing the Excitation Causality between Climate and Political Violence via a Neural Forward-Intensity Poisson Process](https://mlsanity.com/paper/2203.04511/) (2022)
- [Improving Information Cascade Modeling by Social Topology and Dual Role User Dependency](https://mlsanity.com/paper/2204.08529/) (2022)
- [Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance](https://mlsanity.com/paper/2401.09724/) (2024)
- [Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model](https://mlsanity.com/paper/2403.00032/) (2024)
- [Hierarchical Information Enhancement Network for Cascade Prediction in Social Networks](https://mlsanity.com/paper/2403.15257/) (2024)
- [CasFT: Future Trend Modeling for Information Popularity Prediction with Dynamic Cues-Driven Diffusion Models](https://mlsanity.com/paper/2409.16619/) (2024)
- [On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction](https://mlsanity.com/paper/2409.16623/) (2024)
- [Physics-Informed Neural Network with Adaptive Clustering Learning Mechanism for Information Popularity Prediction](https://mlsanity.com/paper/2603.19599/) (2026)
- [Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention](https://mlsanity.com/paper/2605.27584/) (2026)
- [Network World Models as Environments for Algorithm Design on Complex Systems](https://mlsanity.com/paper/2610.01048/) (2026)

## Abstract (arXiv)

An ability to predict the popularity dynamics of individual items within a complex evolving system has important implications in an array of areas. Here we propose a generative probabilistic framework using a reinforced Poisson process to model explicitly the process through which individual items gain their popularity. This model distinguishes itself from existing models via its capability of modeling the arrival process of popularity and its remarkable power at predicting the popularity of individual items. It possesses the flexibility of applying Bayesian treatment to further improve the predictive power using a conjugate prior. Extensive experiments on a longitudinal citation dataset demonstrate that this model consistently outperforms existing popularity prediction methods.