# A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances

arXiv: https://arxiv.org/abs/2005.11041 · HTML: https://mlsanity.com/paper/2005.11041/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 26 of 26)

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- [Hyperdimensional Representation Learning for Node Classification and Link Prediction](https://mlsanity.com/paper/2402.17073/) (2024)
- [H2CGL: Modeling Dynamics of Citation Network for Impact Prediction](https://mlsanity.com/paper/2305.01572/) (2023)
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- [CasCIFF: A Cross-Domain Information Fusion Framework Tailored for Cascade Prediction in Social Networks](https://mlsanity.com/paper/2308.04961/) (2023)
- [Predicting Scientific Impact Through Diffusion, Conformity, and Contribution Disentanglement](https://mlsanity.com/paper/2311.09262/) (2023)
- [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)
- [From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter](https://mlsanity.com/paper/2402.14187/) (2024)
- [AutoCas: Autoregressive Cascade Predictor in Social Networks via Large Language Models](https://mlsanity.com/paper/2502.18040/) (2025)
- [Estimating Online Influence Needs Causal Modeling! Counterfactual Analysis of Social Media Engagement](https://mlsanity.com/paper/2505.19355/) (2025)
- [DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property](https://mlsanity.com/paper/2310.01696/) (2023)
- [On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction](https://mlsanity.com/paper/2409.16623/) (2024)
- [Crafting Tomorrow: The Influence of Design Choices on Fresh Content in Social Media Recommendation](https://mlsanity.com/paper/2410.15174/) (2024)
- [Uncovering Social Network Activity Using Joint User and Topic Interaction](https://mlsanity.com/paper/2506.12842/) (2025)
- [Before the Outrage: Challenges and Advances in Predicting Online Antisocial Behavior](https://mlsanity.com/paper/2507.20614/) (2025)
- [Early Multimodal Prediction of Cross-Lingual Meme Virality on Reddit: A Time-Window Analysis](https://mlsanity.com/paper/2510.05761/) (2025)
- [Physics-Informed Neural Network with Adaptive Clustering Learning Mechanism for Information Popularity Prediction](https://mlsanity.com/paper/2603.19599/) (2026)
- [Benchmarking Multi-Modal Graph-based Social Media Popularity Prediction](https://mlsanity.com/paper/2606.27539/) (2026)
- [Preference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative Retrieval](https://mlsanity.com/paper/2608.30553/) (2026)

## Abstract (arXiv)

The deluge of digital information in our daily life -- from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising -- offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades. Abundant research efforts, both academic and industrial, have aimed to reach a better understanding of the mechanisms driving the spread of information and quantifying the outcome of information diffusion. This article presents a comprehensive review and categorization of information popularity prediction methods, from feature engineering and stochastic processes, through graph representation, to deep learning-based approaches. Specifically, we first formally define different types of information cascades and summarize the perspectives of existing studies. We then present a taxonomy that categorizes existing works into the aforementioned three main groups as well as the main subclasses in each group, and we systematically review cutting-edge research work. Finally, we summarize the pros and cons of existing research efforts and outline the open challenges and opportunities in this field.