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

Hyperedge overlap drives explosive transitions in systems with higher-order interactions

Federico Malizia, Santiago Lamata-Otín, Mattia Frasca, Vito Latora, Jesús Gómez-Gardeñes
Nature Communications
January 9, 2025

Fact-checking information from large language models can decrease headline discernment

Matthew R. DeVerna, Harry Yaojun Yan, Kai-Cheng Yang, Filippo Menczer
PNAS
December 4, 2024

The temporal dynamics of group interactions in higher-order social networks

Iacopo Iacopini, Márton Karsai, Alain Barrat
Nature Communications
August 27, 2024

Recent publications

Hyperedge overlap drives explosive transitions in systems with higher-order interactions

Federico Malizia, Santiago Lamata-Otín, Mattia Frasca, Vito Latora, Jesús Gómez-Gardeñes
Nature Communications
January 9, 2025

Unveiling social vibrancy in urban spaces with app usage

Thomas Collins, Diogo Pacheco, Riccardo Di Clemente, Federico Botta
arXiv
December 19, 2024

Fact-checking information from large language models can decrease headline discernment

Matthew R. DeVerna, Harry Yaojun Yan, Kai-Cheng Yang, Filippo Menczer
PNAS
December 4, 2024

City mobility patterns during the COVID-19 pandemic: analysis of a global natural experiment

Ruth F Hunter, Selin Akaraci, Ruoyu Wang, Rodrigo Reis, Pedro C Hallal, Sandy Pentland, Christopher Millett, Leandro Garcia, Jason Thompson, Kerry Nice, Belen Zapata-Diomedi, Esteban Moro
The Lancet Public Health
November 1, 2024

Narrative-of-Thought: Improving Temporal Reasoning of Large Language Models via Recounted Narratives

Xinliang Frederick Zhang, Nick Beauchamp, Lu Wang
arXiv
October 7, 2024
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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