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

Link prediction with hyperbolic geometry

Maksim Kitsak, Ivan Voitalov, and Dmitri Krioukov
Phys. Rev. Research
October 21, 2020

Isotopy and energy of physical networks

Yanchen Liu, Nima Dehmamy, and Albert-László Barabási
Nature Physics
October 19, 2020

The Unmapped Chemical Complexity of Our Diet

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

Recent publications

Generalized word shift graphs: a method for visualizing and explaining pairwise comparisons between texts

Ryan J. Gallagher, Morgan R. Frank, Lewis Mitchell, Aaron J. Schwartz, Andrew J. Reagan, Christopher M. Danforth & Peter Sheridan Dodds
January 19, 2021

Network comparison and the within-ensemble graph distance

Harrison Hartle, Brennan Klein, Stefan McCabe, Alexander Daniels, Guillaume St-Onge, Charles Murphy and Laurent Hébert-Dufresne
The Royal Society Publishing
November 4, 2020

Who Says What with Whom: Using Bi-Spectral Clustering to Organize and Analyze Social Media Protest Networks

Kenneth Joseph, Ryan J. Gallagher, Brooke Foucault Welles
Computational Communication Research
November 2, 2020

netrd: A library for network reconstruction and graph distances

Stefan McCabe, Leo Torres, Timothy LaRock, Syed Arefinul Haque, Chia-Hung Yang, Harrison Hartle, Brennan Klein
October 30, 2020

Weighted hypersoft configuration model

Ivan Voitalov, Pim van der Hoorn, Maksim Kitsak, Fragkiskos Papadopoulos, and Dmitri Krioukov
Phys. Rev. Research
October 29, 2020
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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