# Factorization Bandits for Online Influence Maximization

arXiv: https://arxiv.org/abs/1906.03737 · HTML: https://mlsanity.com/paper/1906.03737/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2019.

## Built on by (most cited first, 10 of 10)

- [Bandit Multi-linear DR-Submodular Maximization and Its Applications on Adversarial Submodular Bandits](https://mlsanity.com/paper/2305.12402/) (2023)
- [Offline Learning for Combinatorial Multi-armed Bandits](https://mlsanity.com/paper/2501.19300/) (2025)
- [Influence Maximization via Graph Neural Bandits](https://mlsanity.com/paper/2406.12835/) (2024)
- [Online Influence Maximization under Decreasing Cascade Model](https://mlsanity.com/paper/2305.15428/) (2023)
- [Online Influence Maximization under the Independent Cascade Model with Node-Level Feedback](https://mlsanity.com/paper/2109.06077/) (2021)
- [Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization](https://mlsanity.com/paper/2206.14846/) (2022)
- [Targeted Advertising on Social Networks Using Online Variational Tensor Regression](https://mlsanity.com/paper/2208.10627/) (2022)
- [Online Modeling and Monitoring of Dependent Processes under Resource Constraints](https://mlsanity.com/paper/2307.14208/) (2023)
- [Contextual Bandits for Advertising Campaigns: A Diffusion-Model Independent Approach (Extended Version)](https://mlsanity.com/paper/2201.05231/) (2022)
- [Online Influence Maximization: Concept and Algorithm](https://mlsanity.com/paper/2312.00099/) (2023)

## Abstract (arXiv)

We study the problem of online influence maximization in social networks. In this problem, a learner aims to identify the set of "best influencers" in a network by interacting with it, i.e., repeatedly selecting seed nodes and observing activation feedback in the network. We capitalize on an important property of the influence maximization problem named network assortativity, which is ignored by most existing works in online influence maximization. To realize network assortativity, we factorize the activation probability on the edges into latent factors on the corresponding nodes, including influence factor on the giving nodes and susceptibility factor on the receiving nodes. We propose an upper confidence bound based online learning solution to estimate the latent factors, and therefore the activation probabilities. Considerable regret reduction is achieved by our factorization based online influence maximization algorithm. And extensive empirical evaluations on two real-world networks showed the effectiveness of our proposed solution.