Fairness-aware Competitive Bidding Influence Maximization in Social Networks

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.

arXiv commentIEEE Transactions on Computational Social Systems (TCSS), 2023, early access

References in corpus (10)

• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
• Addressing Function Approximation Error in Actor-Critic Methods
• Learning to Communicate with Deep Multi-Agent Reinforcement Learning
• Learning Attentional Communication for Multi-Agent Cooperation
• Multi-agent Inverse Reinforcement Learning for Two-person Zero-sum Games
• Explicit Time Embedding Based Cascade Attention Network for Information Popularity Prediction
• Online Influence Maximization under Linear Threshold Model
• Competitive Influence Maximization: Integrating Budget Allocation and Seed Selection
• Online Competitive Influence Maximization
• Maximal Information Propagation with Budgets