# Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks

arXiv: https://arxiv.org/abs/2105.08291 · HTML: https://mlsanity.com/paper/2105.08291/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 18 May 2021.

## Builds on (most cited first, 8 of 8)

- [Can Cascades be Predicted?](https://mlsanity.com/paper/1403.4608/) (2014)
- [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)
- [The Simple Rules of Social Contagion](https://mlsanity.com/paper/1308.5015/) (2013)
- [Taking census of physics](https://mlsanity.com/paper/1901.02789/) (2019)
- [Deep Collaborative Embedding for information cascade prediction](https://mlsanity.com/paper/2001.06665/) (2020)
- [Measuring similarity in co-occurrence data using ego-networks](https://mlsanity.com/paper/2007.13273/) (2020)
- [A generalized linear threshold model for an improved description of the spreading dynamics](https://mlsanity.com/paper/2008.06834/) (2020)

## Abstract (arXiv)

The prediction for information diffusion on social networks has great practical significance in marketing and public opinion control. It aims to predict the individuals who will potentially repost the message on the social network. One type of method is based on demographics, complex networks and other prior knowledge to establish an interpretable model to simulate and predict the propagation process, while the other type of method is completely data-driven and maps the nodes to a latent space for propagation prediction. Existing latent space design and embedding methods lack consideration for the intervene among users. In this paper, we propose an independent asymmetric embedding method to embed each individual into one latent influence space and multiple latent susceptibility spaces. Based on the similarity between information diffusion and heat diffusion phenomenon, the heat diffusion kernel is exploited in our model and establishes the embedding rules. Furthermore, our method captures the co-occurrence regulation of user combinations in cascades to improve the calculating effectiveness. The results of extensive experiments conducted on real-world datasets verify both the predictive accuracy and cost-effectiveness of our approach.