# On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction

arXiv: https://arxiv.org/abs/2409.16623 · HTML: https://mlsanity.com/paper/2409.16623/

## 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 25 Sep 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec](https://mlsanity.com/paper/1710.02971/) (2017)
- [Neural Temporal Point Processes: A Review](https://mlsanity.com/paper/2104.03528/) (2021)
- [How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision](https://mlsanity.com/paper/2204.04879/) (2022)
- [Learning Structural Node Embeddings Via Diffusion Wavelets](https://mlsanity.com/paper/1710.10321/) (2017)
- [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023)
- [SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity](https://mlsanity.com/paper/1506.02594/) (2015)
- [DeepCas: an End-to-end Predictor of Information Cascades](https://mlsanity.com/paper/1611.05373/) (2016)
- [NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization](https://mlsanity.com/paper/1906.11156/) (2019)
- [Can Cascades be Predicted?](https://mlsanity.com/paper/1403.4608/) (2014)
- [Quantifying Long-Term Scientific Impact](https://mlsanity.com/paper/1306.3293/) (2013)
- [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)
- [Topological Recurrent Neural Network for Diffusion Prediction](https://mlsanity.com/paper/1711.10162/) (2017)
- [Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes](https://mlsanity.com/paper/1401.0778/) (2014)
- [DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction](https://mlsanity.com/paper/2106.03251/) (2021)
- [On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data](https://mlsanity.com/paper/2306.17066/) (2023)
- [A Bayesian approach for predicting the popularity of tweets](https://mlsanity.com/paper/1304.6777/) (2013)
- [Virality Prediction and Community Structure in Social Networks](https://mlsanity.com/paper/1306.0158/) (2013)
- [Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks](https://mlsanity.com/paper/1712.04443/) (2017)
- [CasSeqGCN: Combining Network Structure and Temporal Sequence to Predict Information Cascades](https://mlsanity.com/paper/2110.06836/) (2021)
- [Will This Paper Increase Your h-index? Scientific Impact Prediction](https://mlsanity.com/paper/1412.4754/) (2014)
- [Universality, criticality and complexity of information propagation in social media](https://mlsanity.com/paper/2109.00116/) (2021)
- [Continuous-Time Graph Learning for Cascade Popularity Prediction](https://mlsanity.com/paper/2306.03756/) (2023)

## Abstract (arXiv)

Information popularity prediction is important yet challenging in various domains, including viral marketing and news recommendations. The key to accurately predicting information popularity lies in subtly modeling the underlying temporal information diffusion process behind observed events of an information cascade, such as the retweets of a tweet. To this end, most existing methods either adopt recurrent networks to capture the temporal dynamics from the first to the last observed event or develop a statistical model based on self-exciting point processes to make predictions. However, information diffusion is intrinsically a complex continuous-time process with irregularly observed discrete events, which is oversimplified using recurrent networks as they fail to capture the irregular time intervals between events, or using self-exciting point processes as they lack flexibility to capture the complex diffusion process. Against this background, we propose ConCat, modeling the Continuous-time dynamics of Cascades for information popularity prediction. On the one hand, it leverages neural Ordinary Differential Equations (ODEs) to model irregular events of a cascade in continuous time based on the cascade graph and sequential event information. On the other hand, it considers cascade events as neural temporal point processes (TPPs) parameterized by a conditional intensity function which can also benefit the popularity prediction task. We conduct extensive experiments to evaluate ConCat on three real-world datasets. Results show that ConCat achieves superior performance compared to state-of-the-art baselines, yielding a 2.3%-33.2% improvement over the best-performing baselines across the three datasets.