# Community Exploration: From Offline Optimization to Online Learning

arXiv: https://arxiv.org/abs/1811.05134 · HTML: https://mlsanity.com/paper/1811.05134/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2018.

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

- [Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning](https://mlsanity.com/paper/2106.05065/) (2021)
- [Sequential Community Mode Estimation](https://mlsanity.com/paper/2111.08535/) (2021)

## Abstract (arXiv)

We introduce the community exploration problem that has many real-world applications such as online advertising. In the problem, an explorer allocates limited budget to explore communities so as to maximize the number of members he could meet. We provide a systematic study of the community exploration problem, from offline optimization to online learning. For the offline setting where the sizes of communities are known, we prove that the greedy methods for both of non-adaptive exploration and adaptive exploration are optimal. For the online setting where the sizes of communities are not known and need to be learned from the multi-round explorations, we propose an `upper confidence' like algorithm that achieves the logarithmic regret bounds. By combining the feedback from different rounds, we can achieve a constant regret bound.