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

A structural transition in physical networks

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

Quantifying reputation and success in art

Samuel P. Fraiberger, Roberta Sinatra, Magnus Resch, Christoph Riedl, Albert-László Barabási
Science
November 8, 2018

ε - WGX: Adaptive Edge Probing for Enhancing Incomplete Networks

Soundarajan, Sucheta; Eliassi-Rad, Tina; Gallagher, Brian; Pinar, Ali
Web Science
June 25, 2017

Recent publications

Mapping the physics research space: a machine learning approach

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

Scale-free Networks Well Done

Ivan Voitalov, Pim van der Hoorn, Remco van der Hofstad, and Dmitri Krioukov
Phys. Rev. Research
October 18, 2019

Improving Robustness to Attacks Against Vertex Classification

Benjamin A. Miller, Mustafa Çamurcu, Alexander J. Gomez, Kevin Chan, Tina Eliassi-Rad
MLG'19
August 5, 2019

Taking census of physics

Federico Battiston, Federico Musciotto, Dashun Wang, Albert-László Barabási, Michael Szell & Roberta Sinatra
Nature Reviews Physics
January 8, 2019

A structural transition in physical networks

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

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