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

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

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

Nature's reach: narrow work has broad impact

Alexander J. Gates, Qing Ke, Onur Varol, Albert-László Barabási
November 7, 2019

Recent publications

Spontaneous emergence of groups and signaling diversity in dynamic networks

Zachary Fulker, Patrick Forber, Rory Smead, Christoph Riedl
October 22, 2022

Discovering Where We Excel: How Inclusive Turn-Taking in Conversation Improves Team Performance

Ki-Won Haan, Christoph Riedl, Anita Woolley
ACM Digital Library
October 18, 2022

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

When Does Rivalry Drive Performance?

Tom Grad, Christoph Riedl, Gavin J. Kilduff
CBS Copenhagen Business School
June 13, 2022
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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