Heterogeneous update processes shape information cascades in social networks

Area of Science:

• Network Science
• Information Diffusion
• Computational Social Science

Background:

• Information diffusion models often assume population homogeneity in how individuals acquire and spread information.
• Two primary mechanisms are imitation (simple contagion) and peer influence (complex contagion).
• This study investigates the interplay of different individual update processes within social networks.

Purpose of the Study:

• To analyze the impact of mixing and strategic placement of individuals with distinct information update processes on information cascades.
• To differentiate the roles of Simple Spreaders and Threshold-based Spreaders in information diffusion dynamics.
• To understand how network structure influences the effectiveness of these different spreader types.

Main Methods:

• Simulations on various network structures (random, modular, scale-free, exponential).
• Modeling two types of information spreaders: Simple Spreaders and Threshold-based Spreaders.
• Analyzing cascade sizes and information reach based on spreader proportions and network topology.

Main Results:

• Simple Spreaders amplify cascades in degree-heterogeneous networks (exponential, scale-free).
• In random and modular networks, Simple Spreaders require a critical mass to initiate significant cascades.
• Preferential placement of Threshold-based Spreaders in high-degree nodes impairs information spread, diminishing the effect of Simple Spreaders.

Conclusions:

• The strategic placement of individuals with different information update behaviors is crucial for understanding information diffusion.
• Simple Spreaders are key drivers of widespread cascades in heterogeneous networks.
• Threshold-based Spreaders act as critical regulators, with their effect tunable by threshold values, impacting social phenomena and online information environments.