Fairness-aware Competitive Bidding Influence Maximization in Social Networks

TL;DR

This paper introduces a new influence maximization model considering competitive bidding with fairness, proposing a multi-agent framework and algorithms to optimize bidding strategies in social networks.

Contribution

It presents a novel CBIM problem and a FMCBIM framework with a multi-agent environment and a new algorithm for bidding policy optimization.

Findings

Effective in multiple datasets

Improves influence spread and budget efficiency

Demonstrates robustness of the proposed methods

Abstract

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.