Network performance

how groups coordinate, seek knowledge, optimize and reach success

This work focuses on the social dynamics of knowledge exchange and learning that drive collaboration, collective intelligence, and discovery. Most problem-solving tasks are carried out in group or team settings, and require complex interactions that involve cognitive, social and informational exchange. The goal is to develop rigorous models and techniques to understand how groups reach consensus, achieve breakthroughs, and perform in groups.

Featured publications

Quantifying NFT-driven networks in crypto art

Kishore Vasan, Milán Janosov, Albert-László Barabási
Scientific Reports
February 17, 2022

Network medicine framework for identifying drug-repurposing opportunities for COVID-19

Deisy Morselli Gysi, Ítalo do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Susan Dina Ghiassian, J. J. Patten, Robert A. Davey, Joseph Loscalzo, Albert-László Barabási
PNAS
March 30, 2021

A wealth of discovery built on the Human Genome Project — by the numbers

Alexander J. Gates, Deisy Morselli Gysi, Manolis Kellis & Albert-László Barabási
Nature
February 10, 2021

Recent publications

Multi-fidelity Hierarchical Neural Processes

Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
ACM Digital Library
August 14, 2022

Information access equality on generative models of complex networks

Xindi Wang, Onur Varol, Tina Eliassi-Rad
Springer Open
August 2, 2022

Collective Attention and Collective Intelligence: The Role of Hierarchy and Team Gender Composition

Anita Williams Woolley, Rosalind M. Chow, Anna T. Mayo, Christoph Riedl, Jin Wook Chang
Organization Science
June 7, 2022

Quantifying NFT-driven networks in crypto art

Kishore Vasan, Milán Janosov, Albert-László Barabási
Scientific Reports
February 17, 2022

The hidden influence of communities in collaborative funding of clinical science

Kishore Vasan, Jevin D. West
The Royal Society Publishing
August 25, 2021
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Featured news coverage

Why Experts Reject Creativity

The Atlantic, October 2014

Featured project

The Science of Success project focuses on developing measures and methods to model and predict success in a range of settings that have quantifiable indicators of performance (e.g., science, sports, software development). Driven by the hypothesis that success is not an individual phenomenon, but rather a collective one, we use large-scale data sets to identify patterns of career paths, individual and team performance, and the dynamics of impact and attribution. Findings offer actionable information towards a quantitative evaluation of success in a diverse range of competitive settings, from science to sports to software development.

Major funders

DARPA, Army Research Office, AFOSR