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

Dynamics of ranking

Gerardo Iñiguez, Carlos Pineda, Carlos Gershenson, Albert-László Barabási
Nature Communications
March 28, 2022

Recovery coupling in multilayer networks

Michael M. Danziger, Albert-László Barabási
Nature Communications
February 17, 2022

Measuring algorithmically infused societies

Claudia Wagner, Markus Strohmaier, Alexandra Olteanu, Emre Kıcıman, Noshir Contractor, Tina Eliassi-Rad
June 30, 2021

Recent publications

Designing Ecosystems of Intelligence from First Principles

Karl J Friston, Maxwell J D Ramstead, Alex B Kiefer, Alexander Tschantz, Christopher L Buckley, Mahault Albarracin, Riddhi J Pitliya, Conor Heins, Brennan Klein, Beren Millidge, Dalton A R Sakthivadivel, Toby St Clere Smithe, Magnus Koudahl, Safae Essafi Tremblay, Capm Petersen, Kaiser Fung, Jason G Fox, Steven Swanson, Dan Mapes, Gabriel René
December 2, 2022

Entropy of labeled versus unlabeled networks

Jeremy Paton, Harrison Hartle, Huck Stepanyants, Pim van der Hoorn, and Dmitri Krioukov
American Physical Society
November 17, 2022

Sequential motifs in observed walks

Timothy LaRock, Ingo Scholtes, Tina Eliassi-Rad
Journal of Complex Networks
August 23, 2022

Multi-fidelity Hierarchical Neural Processes

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

Spin glass systems as collective active inference

Conor Heins, Brennan Klein, Daphne Demekas, Miguel Aguilera, Christopher Buckley
July 14, 2022
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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