# Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction

arXiv: https://arxiv.org/abs/2107.05992 · HTML: https://mlsanity.com/paper/2107.05992/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 57 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency](https://mlsanity.com/paper/1404.0900/) (2014)
- [Thompson Sampling for Budgeted Multi-armed Bandits](https://mlsanity.com/paper/1505.00146/) (2015)
- [Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling](https://mlsanity.com/paper/1907.11625/) (2019)
- [Cost-Effective Incentive Allocation via Structured Counterfactual Inference](https://mlsanity.com/paper/1902.02495/) (2019)
- [Predicting Adoption Probabilities in Social Networks](https://mlsanity.com/paper/1309.6369/) (2013)
- [Incentive-Compatible Diffusion Auctions](https://mlsanity.com/paper/2001.06975/) (2020)
- [Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing](https://mlsanity.com/paper/1805.08525/) (2018)

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

- [GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social Networks](https://mlsanity.com/paper/2203.09578/) (2022)
- [Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies](https://mlsanity.com/paper/2601.07884/) (2026)

## Abstract (arXiv)

In recent years, recommendation systems have been widely applied in many domains. These systems are impotent in affecting users to choose the behavior that the system expects. Meanwhile, providing incentives has been proven to be a more proactive way to affect users' behaviors. Due to the budget limitation, the number of users who can be incentivized is restricted. In this light, we intend to utilize social influence existing among users to enhance the effect of incentivization. Through incentivizing influential users directly, their followers in the social network are possibly incentivized indirectly. However, in many real-world scenarios, the topological structure of the network is usually unknown, which makes identifying influential users difficult. To tackle the aforementioned challenges, in this paper, we propose a novel algorithm for exploring influential users in unknown networks, which can estimate the influential relationships among users based on their historical behaviors and without knowing the topology of the network. Meanwhile, we design an adaptive incentive allocation approach that determines incentive values based on users' preferences and their influence ability. We evaluate the performance of the proposed approaches by conducting experiments on both synthetic and real-world datasets. The experimental results demonstrate the effectiveness of the proposed approaches.