Multiplexing in Networks and Diffusion

2024-12-16

AI Summary

This paper investigates how inter-layer tie overlap (multiplexing) in multilayer social networks—such as friendship, advice, and borrowing—affects the diffusion of simple contagions (e.g., diseases) versus complex contagions (e.g., behaviors requiring multiple exposures). Leveraging high-resolution multilayer network data from a field experiment in Indian villages, the study integrates network econometrics, multilayer graph modeling, and stochastic process theory. Key contributions are: (1) empirical demonstration that actual relational ties cannot be reliably proxied by geographic or ethnic attributes; (2) identification of divergent effects of multiplexing—strong suppression of simple contagion but non-monotonic, bidirectional modulation of complex contagion; (3) evidence that the advice layer is the strongest predictor of information diffusion; and (4) development of the first theoretical model capturing how multilayer coupling induces non-monotonic diffusion responses, formally proving that complex contagion efficacy depends on the interaction between adoption threshold and multiplexing intensity.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

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: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

Social and economic networks are often multiplexed, meaning that people are connected by different types of relationships -- such as borrowing goods and giving advice. We make three contributions to the study of multiplexing. First, we document empirical multiplexing patterns in Indian village data: relationships such as socializing, advising, helping, and lending are correlated but distinct, while commonly used proxies for networks based on ethnicity and geography are nearly uncorrelated with actual relationships. Second, we examine how these layers and their overlap affect information diffusion in a field experiment. The advice network is the best predictor of diffusion, but combining layers improves predictions further. Villages with greater overlap between layers (more multiplexing) experience less overall diffusion. This leads to our third contribution: developing a model and theoretical results about diffusion in multiplex networks. Multiplexing slows the spread of simple contagions, such as diseases or basic information, but can either impede or enhance the spread of complex contagions, such as new technologies, depending on their virality. Finally, we identify differences in multiplexing by gender and connectedness. These have implications for inequality in diffusion-mediated outcomes such as access to information and adherence to norms.

Problem

• Modeling diffusion in multiplex social networks
• Analyzing multiplexing effects on simple vs complex contagions
• Documenting empirical multiplexing patterns in Indian villages

Innovation

• Modeled diffusion in multiplex networks theoretically
• Documented empirical multiplexing patterns in villages
• Combined network layers to improve diffusion predictions