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

Hyper-cores promote localization and efficient seeding in higher-order processes

Marco Mancastroppa, Iacopo Iacopini, Giovanni Petri & Alain Barrat
Nature Communications
October 6, 2023

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

Recent publications

Complex network effects on the robustness of graph convolutional networks

Benjamin A. Miller, Kevin Chan, Tina Eliassi-Rad
Applied Network Science
February 21, 2024

Spontaneous emergence of groups and signaling diversity in dynamic networks

Zachary Fulker, Patrick Forber, Rory Smead, Christoph Riedl
Physical Review E
January 23, 2024

Think like a team: Shared mental models predict creativity and problem-solving in space analogs

Leslie A. DeChurch, Alina Lungeanu, Noshir S. Contractor
Acta Astronautica
January 1, 2024

Insights from exact social contagion dynamics on networks with higher-order structures

István Z Kiss, Iacopo Iacopini, Péter L Simon, Nicos Georgiou
Journal of Complex Networks
November 30, 2023

Attacking Shortest Paths by Cutting Edges

Benjamin A. Miller, Zohair Shafi, Wheeler Ruml, Yevgeniy Vorobeychik, Tina Eliassi-Rad, Scott Alfeld
ACM Transactions on Knowledge Discovery from Data
November 30, 2023
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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