Fairness-Aware Competitive Bidding Influence Maximization in Social Networks

Competitive influence maximization (CIM) has been studied for years due to its wide application in many domains. Most current studies primarily focus on the microlevel 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 article, 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 multiagent CBIM (FMCBIM) framework. In this framework, we present a multiagent 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 multiagent CBIM (MCBIM) algorithm to optimize competitors’ bidding policies. Extensive experiments on five datasets show that our work has good efficiency and effectiveness.

I. Introduction

As a fundamental task, influence maximization (IM) [1] has been investigated for nearly two decades owing to the continued prosperity of social networks, where social influence is used to quantify the overall impact of each individual user within the social network [2]. IM has already been widely applied to many domains, e.g., viral marketing [3], [4], social recommendation [5], [6], [7], and information diffusion [8], [9], [10], [11]. The objective of IM is to identify a group of influential nodes (i.e., seed nodes) such that they can activate the maximum number of network nodes given a specific diffusion model.