# Model-Independent Online Learning for Influence Maximization

arXiv: https://arxiv.org/abs/1703.00557 · HTML: https://mlsanity.com/paper/1703.00557/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2017.

## Built on by (most cited first, 3 of 3)

- [A Community-Aware Framework for Social Influence Maximization](https://mlsanity.com/paper/2207.08937/) (2022)
- [Offline Learning for Combinatorial Multi-armed Bandits](https://mlsanity.com/paper/2501.19300/) (2025)
- [A Community-Aware Framework for Influence Maximization with Explicit Accounting for Inter-Community Influence](https://mlsanity.com/paper/2512.23973/) (2025)

## Abstract (arXiv)

We consider influence maximization (IM) in social networks, which is the problem of maximizing the number of users that become aware of a product by selecting a set of "seed" users to expose the product to. While prior work assumes a known model of information diffusion, we propose a novel parametrization that not only makes our framework agnostic to the underlying diffusion model, but also statistically efficient to learn from data. We give a corresponding monotone, submodular surrogate function, and show that it is a good approximation to the original IM objective. We also consider the case of a new marketer looking to exploit an existing social network, while simultaneously learning the factors governing information propagation. For this, we propose a pairwise-influence semi-bandit feedback model and develop a LinUCB-based bandit algorithm. Our model-independent analysis shows that our regret bound has a better (as compared to previous work) dependence on the size of the network. Experimental evaluation suggests that our framework is robust to the underlying diffusion model and can efficiently learn a near-optimal solution.