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

Measuring algorithmically infused societies

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

Dynamic hidden-variable network models

Harrison Hartle, Fragkiskos Papadopoulos, and Dmitri Krioukov
Phys. Rev. E
May 13, 2021

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

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

Recent publications

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
arXiv
July 14, 2022

On Bayesian Mechanics: A Physics of and by Beliefs

Maxwell J D Ramstead, Dalton A R Sakthivadivel, Conor Heins, Magnus Koudahl, Beren Millidge, Lancelot Da Costa, Brennan Klein, Karl J Friston
arxiv
May 23, 2022

Random Simplicial Complexes: Models and Phenomena

Omer Bobrowski, Dmitri Krioukov
Springer Link
April 27, 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