Between 1986 and 1989, he studied physics and engineering at the University of Bucharest; during which time he began researching chaos theory and published three papers.

His doctoral thesis, conducted under the direction of H. Eugene Stanley,[4] was published by Cambridge University Press under the title Fractal Concepts in Surface Growth.

[5][6] After a one-year postdoc at the IBM Thomas J. Watson Research Center, Barabási joined the faculty at the University of Notre Dame in 1995.

Collaborating with his student, Réka Albert, he introduced the Barabási–Albert model,[11] which proposed that growth and preferential attachment are jointly responsible for the emergence of the scale-free property in real-world networks.

In 2009 Science celebrated the ten-year anniversary of Barabási’s groundbreaking discovery by dedicating a special issue to Complex Systems and Networks,[14][15] recognizing his paper as one of the most cited in the journal's history.

In a 2001 paper with Réka Albert and Hawoong Jeong, Barabási demonstrated that networks exhibit robustness to random failures but are highly vulnerable to targeted attacks,[16] a characteristic known as the Achilles' heel property.

Specifically, networks can easily withstand the random failure of a large number of nodes, highlighting their significant robustness.

[30][31] Barabási’s work on nutritional dark matter and food composition, in collaboration with Giulia Menichetti, has fundamentally reshaped our understanding of diet as a complex system and its implication for health.

Barabási's efforts culminated in the 2025 release of GroceryDB[36] and the TrueFood database, that is used by millions on a daily basis, as it reveals the processing levels of foods in US supermarkets.

In his 2008 Nature publication,[39] Barabási utilized anonymized mobile phone data to analyze human mobility, discovering that human movement exhibits a high degree of regularity in time and space, with individuals showing consistent travel distances and a tendency to return to frequently visited locations.

In a subsequent 2010 Science paper,[40] he explored the predictability of human dynamics by analyzing mobile phone user trajectories.

[41] Using this modeling framework, a decade before the COVID-19 pandemic, Barabási predicted the spreading patterns of a virus transmitted through direct contact.

[43] Barabási utilized network control principles to predict the functions of individual neurons within the Caenorhabditis elegans connectome.

[45] Barabási was the recipient of the 2024 Gothenburg Lise Meitner Award;[46] he has also been the recipient of the 2023 Julius Edgar Lilienfeld Prize, the top prize of the American Physical Society,[47] "for pioneering work on the statistical physics of networks that transformed the study of complex systems, and for lasting contributions in communicating the significance of this rapidly developing field to a broad range of audiences."