Fundamental network science

formalized representations of the geometry of multi-dimensional networks

Foundational network science research includes: study of topological data analysis on graphs, reinforcement learning on complex networks, graph embedding and representation learning, scalable algorithms for mining graphs, and anomaly detection. We are also working on a collection of studies developing rigorous approaches to latent-geometric network models, maximum entropy ensembles of random graphs, and their navigability, with applications ranging from neuroscience to quantum gravity and cosmology.

Featured publications

The Unmapped Chemical Complexity of Our Diet

Albert-László Barabási, Giulia Menichetti and Joseph Loscalzo
Nature
December 9, 2019

Mapping the physics research space: a machine learning approach

Matteo Chinazzi, Bruno Gonçalves, Qian Zhang & Alessandro Vespignani
EPJ Data Science
November 6, 2019

A structural transition in physical networks

Nima Dehmamy, Soodabeh Milanlouei & Albert-László Barabási
Nature
November 28, 2018

Recent publications

Understanding the limitations of network online learning

Timothy LaRock, Timothy Sakharov, Sahely Bhadra, Tina Eliassi-Rad
SpringerOpen
September 9, 2020

Computational social science: Obstacles and opportunities

David M. J. Lazer, Alex Pentland, Duncan J. Watts, Sinan Aral, Susan Athey, Noshir Contractor, Deen Freelon, Sandra Gonzalez-Bailon, Gary King, Helen Margetts, Alondra Nelson, Matthew J. Salganik, Markus Strohmaier, Alessandro Vespignani, Claudia Wagner
Science
August 28, 2020

What science can do for democracy: a complexity science approach

Tina Eliassi-Rad, Henry Farrell, David Garcia, Stephan Lewandowsky, Patricia Palacios, Don Ross, Didier Sornette, Karim Thébault, Karoline Wiesner
Humanities and Social Sciences Communications
July 10, 2020

Classical information theory of networks

Filippo Radicchi, Dmitri Krioukov, Harrison Hartle, Ginestra Bianconi
Journal of Physics: Complexity
July 3, 2020

The why, how, and when of representations for complex systems

Leo Torres, Ann S. Blevins, Danielle S. Bassett, Tina Eliassi-Rad
arXiv
June 4, 2020
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Featured news coverage

Featured project

In our project on Scalable Graph Distances, we explore measurements of graph distance in metric spaces, which are required for many graph mining tasks (eg, clustering, anomaly detection). This project explores a formal mathematical foundation covering a family of graph distance measures that overcome common limitations, such as their inability to scale up to millions of nodes and reliance on heuristics. In another collection of studies on latent geometry, we rigorously establish conditions for a given (real) network to have latent geometry. This geometry can then be reliably used in applications ranging from explaining the structure of (optimal) information flows in the brain to providing new approaches to the dark energy problem in cosmology.

Major funders

NSF, Army Research Office