Information Cascades in Human Networks

Milo TrujilloProfessor Gao

Information Cascades• Generalization of Infection Modeling

• Infection based on % threshold of neighbors

• Agent-Based model to examine heterogeneous networks

• Discrete timesteps

Variable Activation Thresholds

• “A Simple Model of Global Cascades on Random Networks” ~ Duncan J. Watts, PNAS 2002

Variable Activity Times• “Diffusion in Networks and the Virtue of Burstiness”, M.

Akbarpour, M. O. Jackson, PNAS 2018

• Poisson, Reversing, and Sticky Agents

Starting Goal• Combine heterogeneous activation thresholds and activity times

• Apply to scale-free networks

• Examine resilience to targeted vs random attacks

• How do you best spread or halt a cascade in human communities?

Model• Random or Scale-Free

networks

• One initial agent infected

• Contagious for 10 turns

• Spreads to all possible neighbors each turn

• No “recovery”

• Simulation ends when no agents contagious

First Study

Activity Comparison

Activity Comparison

Activity Comparison

Larger Scale Study

First Study Conclusions• Scale Free Networks generally safer

• Hubs act as gatekeepers, quarantine cascades

• If a hub is susceptible, can easily spread cascade

• Activity synchronization threatens communities

Second Study

Second Study

Second Study

Second Study Conclusions

• Targeted attacks most effective in scale-free networks with mid-level susceptibility to cascades

• At high and low susceptibility, minimal difference from random attack unless very centralized

Future Work• Change Fixed Topology

• Mix Types of Activity Patterns

• Assortative versus Disassortative Communities