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New study shows why bigger is better, but smaller is ‘smarter’

They say dream big and live bigger, but when it comes to social network, ‘smaller is smarter’, suggested a new study.

Research by physicists from City College of New York has projected that when it comes to important superspreaders of information “smaller is smarter.” This marks as a key change from the popular idea that “bigger is better,” and could have significant impact for an extensive array of social, natural and living networked systems.


Photo Credit: Linus Zoll.

The strength of weak-nodes: a node that despite of being weakly connected in the network is a powerful influencer due to its strategic location connecting highly connected nodes.

Herman A. Makse, one of the physicists in the research team who is a professor in City College’s Levich Institute and a member of the American Physical Society as well stated that the problem of recognizing the nominal set of “influential nodes” in complicated networks for amplifying “viral marketing in social media, optimizing immunization campaigns and protecting networks under attack” is one of the most researched problems in network science.

He added, “So far, only intuitive strategies based mainly on ‘attacking’ the hubs to identify crucial nodes have been developed.’

Makse along with Flaviano Morone, the second physicist of the research team; launched into a mission to solve this problem by employing “rigorous theoretical solutions and systematic benchmarking” – as they described. They also suggested a kind of algorithm, known as “Collective Influence algorithm,” which they suppose overthrows the entire competing modes in the mammoth wide-ranging social networks like Twitter and Facebook that collectively has more than 100 million users.

According to the research team, their study projects that the most prominent superspreaders of information are not those who are most connected with people across the network, but those who are poorly connected people and strategically engulfed by hierarchical coronas of hubs – because top influencers are very counterintuitive.
Morone said that via meticulous mathematical calculations, applying “optimal percolation” and “state-of-the-art spin glass theory,” they found the solution of the “optimal collective influence” trouble in arbitrary networks.

He added, “We show that the set of optimal superspreaders radically differ and is much smaller than that obtained by all previous heuristic rankings, including PageRank, the basis of Google.”

Makse told that this outcome will attract an expansive range of scientists in fields like physics, networks, mathematics, marketing, epidemiology and even to officials who scrutinize the spread of transmittable diseases such as the Ebola outburst.

Morone and Makse’s paper namely “Influence maximization in complex networks through optimal percolation” was published in the July 1 issue of the journal Nature.

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