Predicting influencers has just been made simpler

January 29, 2018, Springer
The average epidemic size according to new theory. Credit: B. Min

Social networks, such as Twitter, thrive on key influencers spreading news. Like information, epidemics also spread from key individuals. To identify the most influential actors in such networks, many studies have, until now, focused on ranking the influence of individual nodes. But these methods are not accurate enough to single out influential spreaders because they fail to take into account the spreading dynamics.

Now, Byungjoon Min from the Institute of Interdisciplinary Physics and Complex Systems, Balearic Island University, Palma de Mallorca, Spain, has calculated for the first time the expected size of epidemic outbreaks when spreading originates from a single seed. In a study published in EPJ B, Min accurately predicts the influence of spreaders in such networks. Applications include viral marketing, efficient immunisation strategies, and identifying the most influential actors in our society.

The author set out to overcome the limitations of previous methods by directly developing a theory for finding influential spreaders. To do so, Min examines the issue from the perspective of the message transmission. He relies on what he calls a susceptible-infected-recovered (SIR) model, typically used for modelling epidemics, capable of describing irreversible spreading processes. The theory presented in this study is based on the precise mapping between the SIR model and the percolation of the message alongside bonds between members of the .

In this study, Min computes the expected size of epidemic outbreaks on networks, starting from a single node. He then validates the theory by means of extensive numerical simulations on artificial and empirical networks with various transmission probabilities. Min's findings show that the location of an initial spreader affects the probability of epidemic outbreaks. However, it doesn't affect the average size of outbreaks once they occur.

Explore further: 'Smaller is smarter' in superspreading of influence in social network

More information: Byungjoon Min, Identifying an influential spreader from a single seed in complex networks via a message-passing approach, The European Physical Journal B (2018). DOI: 10.1140/epjb/e2017-80597-1

Related Stories

Modelling the dynamics of avalanche outbreaks

September 18, 2015

(—The 1918 outbreak of Spanish flu was so unlike other pandemics that it is analogous to a massive natural disaster. The H1N1 virus infected an estimated 500 million people and killed 100 million by some estimates. ...

Location determines social network influence, study finds

August 29, 2010

A team of researchers led by Dr. Hernan Makse, professor of physics at The City College of New York (CCNY), has shed new light on the way that information and infectious diseases proliferate across complex networks. Writing ...

Expert discusses how the opioid epidemic spreads

December 8, 2017

The sale of prescription opioids has risen sharply since 1999, and the number of fatal drug overdoses attributed to the drugs has more than quadrupled. We asked Yale SOM's Marissa King, an expert in social networks who has ...

Recommended for you

CMS gets first result using largest-ever LHC data sample

February 15, 2019

Just under three months after the final proton–proton collisions from the Large Hadron Collider (LHC)'s second run (Run 2), the CMS collaboration has submitted its first paper based on the full LHC dataset collected in ...


Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.