# Physics-Informed Neural Network with Adaptive Clustering Learning Mechanism for Information Popularity Prediction

arXiv: https://arxiv.org/abs/2603.19599 · HTML: https://mlsanity.com/paper/2603.19599/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 68 references that are in the map.
- First posted on arXiv 20 Mar 2026.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next](https://mlsanity.com/paper/2201.05624/) (2022)
- [Physics-informed neural networks (PINNs) for fluid mechanics: A review](https://mlsanity.com/paper/2105.09506/) (2021)
- [SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity](https://mlsanity.com/paper/1506.02594/) (2015)
- [Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2007.03113/) (2020)
- [Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information](https://mlsanity.com/paper/2012.00489/) (2020)
- [DeepCas: an End-to-end Predictor of Information Cascades](https://mlsanity.com/paper/1611.05373/) (2016)
- [STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction](https://mlsanity.com/paper/2209.00225/) (2022)
- [Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity](https://mlsanity.com/paper/1602.06033/) (2016)
- [Popularity Prediction on Social Platforms with Coupled Graph Neural Networks](https://mlsanity.com/paper/1906.09032/) (2019)
- [A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances](https://mlsanity.com/paper/2005.11041/) (2020)
- [Physics-informed deep-learning applications to experimental fluid mechanics](https://mlsanity.com/paper/2203.15402/) (2022)
- [Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes](https://mlsanity.com/paper/1401.0778/) (2014)
- [EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting](https://mlsanity.com/paper/2208.11517/) (2022)
- [Social physics](https://mlsanity.com/paper/2110.01866/) (2021)
- [Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction](https://mlsanity.com/paper/2311.08635/) (2023)
- [Spatio-Temporal Point Processes with Attention for Traffic Congestion Event Modeling](https://mlsanity.com/paper/2005.08665/) (2020)
- [Origin-Destination Network Generation via Gravity-Guided GAN](https://mlsanity.com/paper/2306.03390/) (2023)
- [Capturing Power System Dynamics by Physics-Informed Neural Networks and Optimization](https://mlsanity.com/paper/2103.17004/) (2021)
- [CasGCN: Predicting future cascade growth based on information diffusion graph](https://mlsanity.com/paper/2009.05152/) (2020)
- [Understanding and forecasting lifecycle events in information cascades](https://mlsanity.com/paper/1809.06050/) (2018)
- [CCasGNN: Collaborative Cascade Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/2112.03644/) (2021)
- [Will the Technological Singularity Come Soon? Modeling the Dynamics of Artificial Intelligence Development via Multi-Logistic Growth Process](https://mlsanity.com/paper/2502.19425/) (2025)
- [M3-Net: A Cost-Effective Graph-Free MLP-Based Model for Traffic Prediction](https://mlsanity.com/paper/2508.08543/) (2025)

## Abstract (arXiv)

With society entering the Internet era, the volume and speed of data and information have been increasing. Predicting the popularity of information cascades can help with high-value information delivery and public opinion monitoring on the internet platforms. The current state-of-the-art models for predicting information popularity utilize deep learning methods such as graph convolution networks (GCNs) and recurrent neural networks (RNNs) to capture early cascades and temporal features to predict their popularity increments. However, these previous methods mainly focus on the micro features of information cascades, neglecting their general macroscopic patterns. Furthermore, they also lack consideration of the impact of information heterogeneity on spread popularity. To overcome these limitations, we propose a physics-informed neural network with adaptive clustering learning mechanism, PIACN, for predicting the popularity of information cascades. Our proposed model not only models the macroscopic patterns of information dissemination through physics-informed approach for the first time but also considers the influence of information heterogeneity through an adaptive clustering learning mechanism. Extensive experimental results on three real-world datasets demonstrate that our model significantly outperforms other state-of-the-art methods in predicting information popularity.