# No Time to Observe: Adaptive Influence Maximization with Partial Feedback

arXiv: https://arxiv.org/abs/1609.00427 · HTML: https://mlsanity.com/paper/1609.00427/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2016.

## Built on by (most cited first, 6 of 6)

- [Adaptive Regularized Submodular Maximization](https://mlsanity.com/paper/2103.00384/) (2021)
- [Beyond Pointwise Submodularity: Non-Monotone Adaptive Submodular Maximization subject to Knapsack and k-System Constraints](https://mlsanity.com/paper/2104.04853/) (2021)
- [Robust Adaptive Submodular Maximization](https://mlsanity.com/paper/2107.11333/) (2021)
- [Policy-Embedded Graph Expansion: Networked HIV Testing with Diffusion-Driven Network Samples](https://mlsanity.com/paper/2601.16233/) (2026)
- [Partial-Adaptive Submodular Maximization](https://mlsanity.com/paper/2111.00986/) (2021)
- [Jointly Complementary&Competitive Influence Maximization with Concurrent Ally-Boosting and Rival-Preventing](https://mlsanity.com/paper/2302.09620/) (2023)

## Abstract (arXiv)

Although influence maximization problem has been extensively studied over the past ten years, majority of existing work adopt one of the following models: full-feedback model or zero-feedback model. In the zero-feedback model, we have to commit the seed users all at once in advance, this strategy is also known as non-adaptive policy. In the full-feedback model, we select one seed at a time and wait until the diffusion completes, before selecting the next seed. Full-feedback model has better performance but potentially huge delay, zero-feedback model has zero delay but poorer performance since it does not utilize the observation that may be made during the seeding process. To fill the gap between these two models, we propose Partial-feedback Model, which allows us to select a seed at any intermediate stage. We develop two novel greedy policies that, for the first time, achieve bounded approximation ratios under both uniform and non-uniform cost settings.