Beyond Individual Influence: The Role of Echo Chambers and Community Seeding in the Multilayer three state q-Voter Model

2026-02-20

AI Summary

This study addresses the limited efficacy of traditional influence maximization methods in disseminating complex opinions within multilayer social networks, where echo chambers and cognitive consistency impede diffusion. Building upon a three-state multilayer q-Voter model and leveraging the mABCD benchmark to generate diverse network topologies ranging from open-world to fortress-world configurations, the work systematically evaluates multiple influence maximization strategies. It introduces the concepts of “fortress traps” and “redundancy traps,” revealing how high modularity and perfect inter-layer alignment hinder global propagation. The findings indicate that increasing topological entropy is more effective than enhancing local clustering for fostering diffusion. Experimental results demonstrate that VoteRank consistently outperforms structure-oriented approaches across network types by prioritizing reach diversity, thereby effectively preventing the formation of isolated consensus enclaves.

Technology Category

• Game Theory and Economic Paradigms: Social Choice / Voting
• Search and Optimization: Distributed Search
• Machine Learning: Learning Preferences or Rankings

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Content-based information diffusion
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The diffusion of complex opinions is severely hindered in multilayer social networks by echo chambers and cognitive consistency mechanisms. We investigate Influence Maximization strategies within the 3-state multilayer q-voter model. Utilizing the mABCD benchmark, we simulate social environments ranging from integrated Open Worlds to segregated Fortress Worlds. Our results reveal a topological paradox that we term the"Fortress Trap". In highly modular networks, strategies maximizing local density such as Clique Influence Maximization (CIM) and k-Shell fail to trigger global cascades, creating isolated bunkers of consensus due to the Overkill Effect. Furthermore, we identify a Redundancy Trap in perfectly aligned Clan topologies, where the structural overlap of layers creates a"Perfect Prison,"rendering it the most resistant environment to diffusion. We demonstrate that VoteRank, a strategy that prioritizes diversity of reach over local intensity, consistently outperforms structure-based methods. These findings suggest that, for complex contagion, maximizing topological entropy is more effective than reinforcing local clusters.

Problem

• echo chambers
• influence maximization
• multilayer networks
• complex contagion
• opinion diffusion

Innovation

• multilayer q-voter model
• influence maximization
• echo chambers
• topological entropy
• VoteRank