# CCasGNN: Collaborative Cascade Prediction Based on Graph Neural Networks

arXiv: https://arxiv.org/abs/2112.03644 · HTML: https://mlsanity.com/paper/2112.03644/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 7 Dec 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [DeepCas: an End-to-end Predictor of Information Cascades](https://mlsanity.com/paper/1611.05373/) (2016)
- [Can Cascades be Predicted?](https://mlsanity.com/paper/1403.4608/) (2014)
- [Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes](https://mlsanity.com/paper/1401.0778/) (2014)
- [Modeling and Predicting Popularity Dynamics of Microblogs using Self-Excited Hawkes Processes](https://mlsanity.com/paper/1503.02754/) (2015)
- [CasGCN: Predicting future cascade growth based on information diffusion graph](https://mlsanity.com/paper/2009.05152/) (2020)
- [CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling](https://mlsanity.com/paper/2110.05820/) (2021)

## Built on by (most cited first, 1 of 1)

- [Physics-Informed Neural Network with Adaptive Clustering Learning Mechanism for Information Popularity Prediction](https://mlsanity.com/paper/2603.19599/) (2026)

## Abstract (arXiv)

Cascade prediction aims at modeling information diffusion in the network. Most previous methods concentrate on mining either structural or sequential features from the network and the propagation path. Recent efforts devoted to combining network structure and sequence features by graph neural networks and recurrent neural networks. Nevertheless, the limitation of spectral or spatial methods restricts the improvement of prediction performance. Moreover, recurrent neural networks are time-consuming and computation-expensive, which causes the inefficiency of prediction. Here, we propose a novel method CCasGNN considering the individual profile, structural features, and sequence information. The method benefits from using a collaborative framework of GAT and GCN and stacking positional encoding into the layers of graph neural networks, which is different from all existing ones and demonstrates good performance. The experiments conducted on two real-world datasets confirm that our method significantly improves the prediction accuracy compared to state-of-the-art approaches. What's more, the ablation study investigates the contribution of each component in our method.