# DeepCas: an End-to-end Predictor of Information Cascades

arXiv: https://arxiv.org/abs/1611.05373 · HTML: https://mlsanity.com/paper/1611.05373/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2016.

## Built on by (most cited first, 30 of 48)

- [A Survey on Embedding Dynamic Graphs](https://mlsanity.com/paper/2101.01229/) (2021)
- [Unsupervised Path Representation Learning with Curriculum Negative Sampling](https://mlsanity.com/paper/2106.09373/) (2021)
- [Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem](https://mlsanity.com/paper/2106.10785/) (2021)
- [Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning -- Extended Version](https://mlsanity.com/paper/2203.16110/) (2022)
- [A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization](https://mlsanity.com/paper/2211.03074/) (2022)
- [Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge](https://mlsanity.com/paper/2105.12419/) (2021)
- [CCGL: Contrastive Cascade Graph Learning](https://mlsanity.com/paper/2107.12576/) (2021)
- [Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction](https://mlsanity.com/paper/2305.17417/) (2023)
- [Before It's Too Late: A State Space Model for the Early Prediction of Misinformation and Disinformation Engagement](https://mlsanity.com/paper/2502.04655/) (2025)
- [H2CGL: Modeling Dynamics of Citation Network for Impact Prediction](https://mlsanity.com/paper/2305.01572/) (2023)
- [Link Prediction without Graph Neural Networks](https://mlsanity.com/paper/2305.13656/) (2023)
- [Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning](https://mlsanity.com/paper/2104.01711/) (2021)
- [Learnability of Competitive Threshold Models](https://mlsanity.com/paper/2205.03750/) (2022)
- [Zoo Guide to Network Embedding](https://mlsanity.com/paper/2305.03474/) (2023)
- [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)
- [Cascade-based Randomization for Inferring Causal Effects under Diffusion Interference](https://mlsanity.com/paper/2405.12340/) (2024)
- [R-ODE: Ricci Curvature Tells When You Will be Informed](https://mlsanity.com/paper/2405.17282/) (2024)
- [Contrastive Learning for Implicit Social Factors in Social Media Popularity Prediction](https://mlsanity.com/paper/2410.09345/) (2024)
- [Interacting Large Language Model Agents. Interpretable Models and Social Learning](https://mlsanity.com/paper/2411.01271/) (2024)
- [From Leakage to Fidelity: Reliable Benchmarking for Temporal Cascade Prediction](https://mlsanity.com/paper/2510.25348/) (2025)
- [Network representation learning systematic review: ancestors and current development state](https://mlsanity.com/paper/2109.07583/) (2021)
- [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)
- [Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction](https://mlsanity.com/paper/2204.08229/) (2022)
- [Revisiting Information Cascades in Online Social Networks](https://mlsanity.com/paper/2208.00904/) (2022)
- [Explicit Time Embedding Based Cascade Attention Network for Information Popularity Prediction](https://mlsanity.com/paper/2308.09976/) (2023)
- [The Role of Network and Identity in the Diffusion of Hashtags](https://mlsanity.com/paper/2407.12771/) (2024)
- [Attribute-Enhanced Similarity Ranking for Sparse Link Prediction](https://mlsanity.com/paper/2412.00261/) (2024)
- [AutoCas: Autoregressive Cascade Predictor in Social Networks via Large Language Models](https://mlsanity.com/paper/2502.18040/) (2025)

## Abstract (arXiv)

Information cascades, effectively facilitated by most social network platforms, are recognized as a major factor in almost every social success and disaster in these networks. Can cascades be predicted? While many believe that they are inherently unpredictable, recent work has shown that some key properties of information cascades, such as size, growth, and shape, can be predicted by a machine learning algorithm that combines many features. These predictors all depend on a bag of hand-crafting features to represent the cascade network and the global network structure. Such features, always carefully and sometimes mysteriously designed, are not easy to extend or to generalize to a different platform or domain. Inspired by the recent successes of deep learning in multiple data mining tasks, we investigate whether an end-to-end deep learning approach could effectively predict the future size of cascades. Such a method automatically learns the representation of individual cascade graphs in the context of the global network structure, without hand-crafted features and heuristics. We find that node embeddings fall short of predictive power, and it is critical to learn the representation of a cascade graph as a whole. We present algorithms that learn the representation of cascade graphs in an end-to-end manner, which significantly improve the performance of cascade prediction over strong baselines that include feature based methods, node embedding methods, and graph kernel methods. Our results also provide interesting implications for cascade prediction in general.