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

TL;DR

This paper explores how echo chambers and community seeding affect opinion diffusion in multilayer social networks, revealing that diversity-focused strategies outperform local density methods in complex contagion scenarios.

Contribution

It introduces the concept of the Fortress and Redundancy Traps in multilayer networks and demonstrates the effectiveness of VoteRank over traditional influence maximization strategies.

Findings

Clique Influence Maximization and k-Shell fail in modular networks

VoteRank outperforms structure-based influence strategies

Maximizing topological entropy enhances diffusion effectiveness

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.