All glitters are not gold, similarly a new study by CCNY researchers proved that larger may not be smarter. The study reveals that ‘smaller is smarter’ when it comes to influential superspreaders of data within the social networks.
According to the researchers, the top influencers are apparently not the most related individuals within the social community. Top influencers are highly counterintuitive: weakly connected people strategically overwhelmed by hierarchical coronas of hubs are the most dominant influencers.
The research was conducted by City College of New York physicists Flaviano Morone and Hernan A. Makse and for the same Makse revealed that there has been a massive shift in perception of people against “bigger is better” and this could have huge consequences for a broad range of social, natural and living networked systems.
“The problem of identifying the minimal set of influential nodes in complex networks for maximizing viral marketing in social media, optimizing immunization campaigns and protecting systems under attack is one of the most studied problems in network science,” said Makse, a professor in City College’s Levich Institute and a fellow of the American Physical Society. “So far, only intuitive strategies based mainly on ‘attacking’ the hubs to identify crucial nodes have been developed.”
Morone and Makse further explained the method applied to solve the problem, “rigorous theoretical solutions and systematic benchmarking.” A scalable algorithm, Collective Influence algorithm has been proposed by them, for the same issue. They believe that will beat all the competing methods in massively large-scale, which are currently being used by social networks like Twitter and Facebook with more than 100 million users.
“Through rigorous mathematical calculations, employing optimal percolation and state-of-the-art spin glass theory, we solved the optimal collective influence problem in random networks,” said Morone. “We show that the set of optimal superspreaders radically differ and is much smaller than that obtained by all previous heuristics rankings, including PageRank, the basis of Google.”
As a result, the study will provide insight into mathematics, epidemiology, networks and marketing. Scientists believe that the survey will also help in monitoring the spreading up of contagious diseases like Ebola.
The study appeared in the scientific journal Nature.Tags: algorithm, facebook, social media, study, twitter