# Online Influence Maximization under Decreasing Cascade Model

arXiv: https://arxiv.org/abs/2305.15428 · HTML: https://mlsanity.com/paper/2305.15428/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 6 of 6)

- [Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms](https://mlsanity.com/paper/1407.8339/) (2014)
- [Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications](https://mlsanity.com/paper/1703.01610/) (2017)
- [Online Influence Maximization (Extended Version)](https://mlsanity.com/paper/1506.01188/) (2015)
- [Online Influence Maximization under Linear Threshold Model](https://mlsanity.com/paper/2011.06378/) (2020)
- [Influence Maximization with Bandits](https://mlsanity.com/paper/1503.00024/) (2015)
- [Factorization Bandits for Online Influence Maximization](https://mlsanity.com/paper/1906.03737/) (2019)

## Built on by (most cited first, 2 of 2)

- [Hybrid Combinatorial Multi-armed Bandits with Probabilistically Triggered Arms](https://mlsanity.com/paper/2512.21925/) (2025)
- [Online Influence Maximization: Concept and Algorithm](https://mlsanity.com/paper/2312.00099/) (2023)

## Abstract (arXiv)

We study online influence maximization (OIM) under a new model of decreasing cascade (DC). This model is a generalization of the independent cascade (IC) model by considering the common phenomenon of market saturation. In DC, the chance of an influence attempt being successful reduces with previous failures. The effect is neglected by previous OIM works under IC and linear threshold models. We propose the DC-UCB algorithm to solve this problem, which achieves a regret bound of the same order as the state-of-the-art works on the IC model. Extensive experiments on both synthetic and real datasets show the effectiveness of our algorithm.