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

Improving the performance and interpretability on medical datasets using graphical ensemble feature selection

Enzo Battistella, Dina Ghiassian, Albert-László Barabási
Bioinformatics
June 5, 2024

Distributed constrained combinatorial optimization leveraging hypergraph neural networks

Nasimeh Heydaribeni, Xinrui Zhan, Ruisi Zhang, Tina Eliassi-Rad & Farinaz Koushanfar
Nature Machine Intelligence
May 30, 2024

Unsupervised embedding of trajectories captures the latent structure of scientific migration

Dakota Murray, Jisung Yoon, Sadamori Kojaku, Rodrigo Costas, Woo-Sung Jung, Staša Milojević, Yong-Yeol Ahn
PNAS
November 13, 2023

Recent publications

Measuring spatial distances in causal sets via causal overlaps

Marián Boguñá and Dmitri Krioukov
Physical Review D
July 8, 2024

Diameter of Compact Riemann Surfaces

Huck Stepanyants, Alan Beardon, Jeremy Paton & Dmitri Krioukov
Computational Methods and Function Theory
June 27, 2024

Improving the performance and interpretability on medical datasets using graphical ensemble feature selection

Enzo Battistella, Dina Ghiassian, Albert-László Barabási
Bioinformatics
June 5, 2024

Random hyperbolic graphs in 𝑑+1 dimensions

Gabriel Budel, Maksim Kitsak, Rodrigo Aldecoa, Konstantin Zuev, Dmitri Krioukov
Physical Review
May 30, 2024

Distributed constrained combinatorial optimization leveraging hypergraph neural networks

Nasimeh Heydaribeni, Xinrui Zhan, Ruisi Zhang, Tina Eliassi-Rad & Farinaz Koushanfar
Nature Machine Intelligence
May 30, 2024
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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