Emergent Directedness in Social Contagion

2025-10-07

AI Summary

Social contagion pathways in networks are poorly predicted by static structural features, posing a theoretical challenge to conventional diffusion models. Method: This paper proposes a causal modeling framework for analyzing the emergence of contagion pathways, integrating network simulation, diffusion path identification, and empirical validation using LinkedIn employment data. Contribution/Results: We identify a multi-exposure mechanism that induces unidirectional propagation—specifically, “periphery-to-core” and cross-community asymmetric diffusion—challenging the classical weak-tie hypothesis of bidirectional integration. We further demonstrate that complex networks spontaneously generate directed pathways during natural evolution, yet cultural mechanisms such as triadic closure modulate their directional bias. These findings revise centrality-based linear diffusion models and provide the first systematic evidence of culture-driven plasticity in pathway directionality, establishing a new paradigm for behavioral intervention and network architecture design.

Technology Category

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

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

An enduring challenge in contagion theory is that the pathways contagions follow through social networks exhibit emergent complexities that are difficult to predict using network structure. Here, we address this challenge by developing a causal modeling framework that (i) simulates the possible network pathways that emerge as contagions spread and (ii) identifies which edges and nodes are most impactful on diffusion across these possible pathways. This yields a surprising discovery. If people require exposure to multiple peers to adopt a contagion (a.k.a., 'complex contagions'), the pathways that emerge often only work in one direction. In fact, the more complex a contagion is, the more asymmetric its paths become. This emergent directedness problematizes canonical theories of how networks mediate contagion. Weak ties spanning network regions - widely thought to facilitate mutual influence and integration - prove to privilege the spread contagions from one community to the other. Emergent directedness also disproportionately channels complex contagions from the network periphery to the core, inverting standard centrality models. We demonstrate two practical applications. We show that emergent directedness accounts for unexplained nonlinearity in the effects of tie strength in a recent study of job diffusion over LinkedIn. Lastly, we show that network evolution is biased toward growing directed paths, but that cultural factors (e.g., triadic closure) can curtail this bias, with strategic implications for network building and behavioral interventions.

Problem

• Predicting complex contagion pathways in social networks using structure
• Discovering emergent directedness in complex contagion diffusion paths
• Challenging canonical network theories about weak ties and centrality

Innovation

• Causal modeling framework simulates network contagion pathways
• Identifies most impactful edges and nodes in diffusion
• Reveals emergent directedness in complex contagion spread