New! Sign up for our email newsletter on Substack.

Researchers use science to predict success

We all want to know the secret to suc­cess and physi­cists are no dif­ferent. Like the rest of the aca­d­emic com­mu­nity, physi­cists rely on var­ious quan­ti­ta­tive fac­tors to deter­mine whether a researcher will enjoy long-​​term suc­cess. These fac­tors help deter­mine every­thing from grant approvals to hiring deci­sions. The only problem with this method, according to Dis­tin­guished Uni­ver­sity Pro­fessor of Physics Albert-​​László Barabási, is a known lack of pre­dic­tive power.

Impact factor, for example, is a mea­sure of a schol­arly journal’s impact on the field over time while the Hirsch index quan­ti­fies an indi­vidual researcher’s suc­cess. While these models do a good job of rep­re­senting past accom­plish­ments, they are not able to pre­dict the future for young researchers and new papers.

In a paper released Thursday in the journal Sci­ence, Barabási—a world-​​renowned net­work sci­en­tist who has joint appoint­ments in the Col­lege of Sci­ence and the Col­lege of Com­puter and Infor­ma­tion Sci­ence—and his team at Northeastern’s Center for Com­plex Net­work Research offer a new math­e­mat­ical model for quan­ti­fying impact that goes a step fur­ther in its ability to fore­cast long-​​term success.

“Nov­elty and impor­tance depend on so many intan­gible and sub­jec­tive dimen­sions that it is impos­sible to objec­tively quan­tify them all,” write the study’s authors. “Here, we bypass the need to eval­uate a paper’s intrinsic value.”

The team exam­ined the cita­tion his­to­ries of thou­sands of schol­arly physics arti­cles pub­lished between 1893 and 2010, hoping to find some pat­terns. “At first what we saw was true chaos,” explained Barabási. Some arti­cles met with plenty of atten­tion in the first year after pub­li­ca­tion but interest quickly fell there­after, others took four or five years before nose-​​diving, while still others never expe­ri­enced a spike.

To sort through this apparent dis­order, the team iden­ti­fied three mech­a­nisms that seemed fun­da­mental to the way a paper gen­er­ates cita­tions: its orig­i­nality, its age, and the number of cita­tions it has already accrued.

The team trans­lated each of these con­cepts into a math­e­mat­ical equa­tion and then com­bined the results to create a new model for rep­re­senting cita­tion pat­terns over the course of a paper’s life­time. The new model suc­cess­fully matched the cita­tion his­tory of every one of the 463,348 papers they examined.

Unlike any of the existing impact mea­sures out there, Barabási’s new model has the added func­tion­ality of being able to pre­dict long-​​term cita­tion his­to­ries based on just the first few years of data. The findings—which the team val­i­dated in fields beyond physics, including biology, chem­istry, and the social sciences—provide a new, arguably more effec­tive, tool for quan­ti­fying aca­d­e­mi­cians’ success.

The research con­tinues Northeastern’s ground­breaking work in net­work sci­ence. For instance, Barabási is also working to build the human dis­ea­some—a net­work of cel­lular and genetic inter­ac­tions that will help sci­en­tists better under­stand the causes of all kinds of ill­nesses and ail­ments. Researchers are also using net­work sci­ence to study pol­i­tics, social media, and the spread of epi­demic con­ta­gions. And this summer, North­eastern launched the nation’s first doc­toral pro­gram in net­work science.


Did this article help you?

If you found this piece useful, please consider supporting our work with a small, one-time or monthly donation. Your contribution enables us to continue bringing you accurate, thought-provoking science and medical news that you can trust. Independent reporting takes time, effort, and resources, and your support makes it possible for us to keep exploring the stories that matter to you. Together, we can ensure that important discoveries and developments reach the people who need them most.