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

TL;DR

This paper introduces a novel algorithm to identify influential users in unknown social networks and adaptively allocate incentives to maximize influence under budget constraints, using behavioral data without requiring network topology.

Contribution

The paper presents a new method for discovering influential users and allocating incentives adaptively in unknown social networks, addressing the challenge of limited network information.

Findings

The proposed algorithm effectively identifies influential users without network topology.

Adaptive incentive allocation improves influence spread under budget restrictions.

Experimental results validate the approach on synthetic and real datasets.

Abstract

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.