A new algorithm can predict which groups, such as rappers, are likely to work together in the future based on their past partnerships.
In 2013, for example, rap artists Gucci Mane and Young Thug collaborated on the song “Anything” on a Mane mixtape, and both later appeared on Waka Flocka Flame’s track “Fell.”
In 2014, Young Thug twice featured on Travis Scott’s mixtape, Days Before Rodeo, and both Mane and Scott appeared on Kanye West’s 2016 ensemble track “Champions.”
These pair-ups made it highly likely that all three artists would collaborate with each other, according to the researchers who developed the algorithm. And sure enough, in 2016 Scott and Mane appeared on Young Thug’s track “Floyd Mayweather.”
OTHER GROUPS, TOO
The rap collaboration is among the examples researchers explore in a new paper in Proceedings of the National Academy of Sciences.
The researchers created and studied 19 data sets across a wide range of areas, including rap artists, coauthors of academic papers, components of new pharmaceuticals, tags used to label topics discussed in online chat rooms, Congressional members who cowrote bills or served together on committees, and illicit drug combinations that preceded emergency room visits.
“We asked, ‘Can we predict which new group interactions will appear in the future given data up to the present?'” says coauthor Austin Benson, an assistant professor of computer science at Cornell University.
“The application might be which new teams are going to form at a company, or which new groups of friends are going to form, or which new substances will go into combination to form a drug. People had done this with two things at a time, but they hadn’t really done this with groups before,” Benson says.
OPEN AND CLOSED TRIANGLES
The researchers performed their analysis by looking at which people or entities combined in pairs, and found that when three entities cooperated with each other in pairs—an open triangle—it became highly likely that all three would come together into a group, or a closed triangle. The likelihood of a closed triangle rose as the number of collaborations between each of the pairs increased.
For example, the researchers examined HIV anti-retroviral drugs, which different types of gene inhibitors may compose. They identified an open triangle between two types of gene inhibitors and a breast cancer resistance protein inhibitor. Evotaz, an HIV combination drug using all three medications, came out six years after the “open triangle” formed.
“This is the type of group interaction we’re hoping to predict,” Benson says.
There are several other potential applications for a method that predicts group collaborations, Benson says. It could be useful in predicting friend and contact networks, and social networks like Facebook could use it to suggest members for a group or invitees to an event. Likely predictions of coauthors could be useful in suggesting collaborations among researchers. Predictions about combinations of illegal drugs and prescription medication could help hospital staff prepare for patients facing adverse effects.
The researchers checked the accuracy of their predictions using an algorithm that compared them retroactively against actual interactions and collaborations. They tested around 20 different models, and found that the most effective combined several relatively uncomplicated computations to predict the likelihood of closed triangles.
“Some really simple methods worked, which is not always the case in our field,” Benson says.
Additional researchers on the project are from New York University, Cornell, and the Massachusetts Institute of Technology. The National Science Foundation and a Simons Investigator Award supported the research.
Source: Cornell University
Original Study DOI: 10.1073/pnas.1800683115
Source Published in Futurity