Multiple Influences Maximization Under Dynamic Link Strength in Multi-Agent Systems: The Competitive and Cooperative Cases.

Abstract

This article addresses the issue of multiple influences maximization under dynamic link strength (MIMDLS) in multi-agent systems (MASs). Initially, a novel model for dynamic link strength within MASs is suggested to facilitate the simulation of multiple influences diffusion. Subsequently, the MIMDLS problem is formulated with both competitive and cooperative scenarios being examined. In response, two diffusion models, specifically the competitive multiple influences independent cascade (Cp-MIIC) model and the cooperative multiple influences linear threshold (Cr-MILT) model, are designed for MASs. Furthermore, a distributed deep reinforcement learning (DRL) framework is established based on MASs by incorporating asynchronous training and updating processes for seed selection in the context of multiple influences. Moreover, the developed distributed DRL algorithm encompasses the estimation of Q value as well as the management of constraints within Cp-MIIC and Cr-MILT models. Finally, comprehensive experiments are conducted to: 1) validate the effectiveness and efficiency of the proposed models and algorithms in terms of multiple influence diffusion and 2) benchmark their performance against state-of-the-art methods.