Hunt for the Super-Spreaders — A Complex Networks Approach

Contagious diseases, such as SARS and COVID-19, bring a large amount of damage to human’s life and world economy. Pathogens spread among individuals through the contact network. It is observed that most social networks show a power-law degree distribution, implying that hubs exist in these networks. Finding underlying super-spreaders (hubs) and isolating or immunizing them can decrease the pathogen spreading dramatically. In this project, we propose a new framework based on “Biased Friendship Paradox” (BFP) to identify latent super-spreaders effectively and efficiently with- out global knowledge of the network. Using this method to guide isolation and immunization will save a large amount of time and resources.

Faculty Supervisor:

Yuanzhu Chen


Zhihao Dong


Verafin Inc.


Computer science


Information and cultural industries


Memorial University of Newfoundland



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