  Competitive Influence Maximization (CIM) has been studied for years due to
its wide application in many domains. Most current studies primarily focus on
the micro-level optimization by designing policies for one competitor to defeat
its opponents. Furthermore, current studies ignore the fact that many
influential nodes have their own starting prices, which may lead to inefficient
budget allocation. In this paper, we propose a novel Competitive Bidding
Influence Maximization (CBIM) problem, where the competitors allocate budgets
to bid for the seeds attributed to the platform during multiple bidding rounds.
To solve the CBIM problem, we propose a Fairness-aware Multi-agent Competitive
Bidding Influence Maximization (FMCBIM) framework. In this framework, we
present a Multi-agent Bidding Particle Environment (MBE) to model the
competitors' interactions, and design a starting price adjustment mechanism to
model the dynamic bidding environment. Moreover, we put forward a novel
Multi-agent Competitive Bidding Influence Maximization (MCBIM) algorithm to
optimize competitors' bidding policies. Extensive experiments on five datasets
show that our work has good efficiency and effectiveness.
