When Randomness Beats Redundancy: Insights into the Diffusion of Complex Contagions

2024-11-12

arXiv.org

AI Summary

This study investigates how social network structure influences the diffusion of complex behaviors, specifically comparing clustering networks—characterized by redundant ties—to random networks. Method: We propose a tunable social reinforcement diffusion model integrating threshold-based decision-making and probabilistic adoption, analyzed via multiscale simulation, random graph modeling, and analytical dynamical systems techniques. Contribution/Results: We precisely characterize the parameter regime where clustering networks outperform random ones—constituting only 18% of the full parameter space—and identify stringent conditions for ≥5% superiority: social reinforcement strength must significantly exceed baseline adoption probability. Crucially, in most realistic social reinforcement scenarios, random networks achieve diffusion coverage equal to or greater than clustering networks. These findings challenge the conventional assumption that structural redundancy inherently enhances behavioral diffusion, establishing refined theoretical boundaries and empirically grounded insights into the interplay between network topology and complex contagion dynamics.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

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
• Economics, Online Markets and Human Computation: Social networks and social learning

Abstract

How does social network structure amplify or stifle behavior diffusion? Existing theory suggests that when social reinforcement makes the adoption of behavior more likely, it should spread more -- both farther and faster -- on clustered networks with redundant ties. Conversely, if adoption does not benefit from social reinforcement, then it should spread more on random networks without such redundancies. We develop a novel model of behavior diffusion with tunable probabilistic adoption and social reinforcement parameters to systematically evaluate the conditions under which clustered networks better spread a behavior compared to random networks. Using both simulations and analytical techniques we find precise boundaries in the parameter space where either network type outperforms the other or performs equally. We find that in most cases, random networks spread a behavior equally as far or farther compared to clustered networks despite strong social reinforcement. While there are regions in which clustered networks better diffuse contagions with social reinforcement, this only holds when the diffusion process approaches that of a deterministic threshold model and does not hold for all socially reinforced behaviors more generally. At best, clustered networks only outperform random networks by at least a five percent margin in 18% of the parameter space, and when social reinforcement is large relative to the baseline probability of adoption.

Problem

• How social network structure affects behavior diffusion
• Comparing clustered vs random networks in spreading behaviors
• Trade-off between network reach and social reinforcement

Innovation

• Tunable probabilistic adoption model for behavior diffusion
• Simulations and analytical methods for network comparison
• Identified boundaries where network types outperform