Social Contagion and Associative Diffusion in Multilayer Network

TL;DR

This paper introduces a multilayer network model combining social contagion and associative diffusion to better understand cultural variation emergence, demonstrating complex dynamics across different network types.

Contribution

It develops a novel multilayer network framework integrating contagion and associative diffusion, capturing their combined effects on cultural transmission.

Findings

Nontrivial dynamics observed between contagion and associative diffusion.

Model behavior varies across fully connected, small-world, and scale-free networks.

Highlights the importance of considering both models simultaneously.

Abstract

The question that how cultural variation emerges has drawn lots of interest in sociological inquiry. Sociologists predominantly study such variation through the lens of social contagion, which mostly attributes cultural variation to the underlying structural segregation, making it epiphenomenal to the pre-existing segregated structure. On the other hand, arguing culture doesn't spread like a virus, an alternative called associative diffusion was proposed, in which cultural transmission occurs not at the preference of practices, but at the association between practices. The associative diffusion model then successfully explains cultural variation without attributing it to a segregated social structure. The contagion model and associative diffusion model require different types of relationships and interactions to make cultural transmission possible. In reality, both types of relationships exist. In light of this concern, we proposed combining the two models with the multilayer network framework. On one layer, agents casually observed the behaviors of others, updating their belief about the association between practices; on another layer, agents' preference of practices are directly influenced by closed others. In the meantime, the constraint satisfaction between preference and association is used to link the update of both, thereby making each individual a coherent entity in terms of preference and association. Using this approach, we entangle the effect of social contagion and associative diffusion through multilayer networks. For the baseline, we explore the model dynamics on three common network models: fully connected, small-world, and scale-free. The results show nontrivial dynamics between the two extremes of the contagion model and the associative diffusion model, justifying our claim that it is necessary to consider the two models at the same time.