About

Albert-László Barabási is the Robert Gray Dodge Professor of Network Science and a University Distinguished Professor at Northeastern University. His work primarily focuses on the study of complex networks across various fields, including biological systems, social interactions, the Internet, and ecological systems. Barabási's research challenges traditional models of random graph theory by investigating the topology of networks such as the World Wide Web, cellular, and social networks, revealing that they follow a common blueprint characterized by scale-free properties. This discovery has led to a significant paradigm shift in understanding how networks function and their robustness, error tolerance, and dynamics. His contributions extend to applying network theory to biological systems, aiming to uncover the chemical architecture of cells, and to evaluating the effectiveness of traditional Chinese medicine through network science. Barabási's work has earned him recognition as an NAS Fellow and an AAAS Fellow, and he was elected to the National Academy of Sciences for his pioneering research in network science. He is also involved in interdisciplinary projects, including visualizing misinformation spread, mapping the chemical makeup of foods, and expanding the field through initiatives like the Network Science Institute, including its expansion to London. His research has had a broad impact, influencing fields from medicine to social sciences, and has been recognized with prestigious awards such as the American Physical Society's Julius Edgar Lilienfeld Prize.

Research topics

• Political Science
• Sociology
• Psychology
• Computer Science
• Social Science
• Social psychology
• Public relations
• Pedagogy
• Pathology
• Gerontology
• Environmental health
• Mathematics
• Data science
• Demographic economics
• Engineering
• Economics
• Mathematics education
• Engineering ethics
• Medicine

Selected publications

• Hungary’s chance to rebuild science

  Science · 2026-05-14

  article1st authorCorresponding

  Last month's parliamentary elections ended Prime Minister Viktor Orbán's 16-year rule, giving Hungary something more consequential than a change of government: a chance to show the world how to rebuild science after political control. With a two-thirds parliamentary majority, the new leadership has the mandate and the constitutional power to rebuild Hungary's scientific enterprise around merit, and resistance against future interference. If Hungary gets this right, it will offer a model that matters far beyond its borders.

  PublisherDOI

• The aging genome exhibits organized vulnerability to somatic mutations

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-05-22

  articleOpen accessSenior authorCorresponding

  Somatic mutations accumulate throughout life and have been hypothesized to drive organismal decline. Yet whether these mutations are distributed randomly or whether cells shield their most critical components has remained unresolved. Here we analyze over a million somatic mutations across thirteen human tissues, finding that the aging genome exhibits organized vulnerability, captured by the existence of hypo-mutated genes and longevity-associated pathways that have significantly lower mutation burden. Highly connected network hubs are systematically protected from mutation, while peripheral, condition-specific genes accumulate disproportionate burdens. We show that this organized vulnerability arises from the interplay of two independent mechanisms: transcription-coupled repair, and selective filtering. Finally, we validate our findings under experimental mutagenesis, demonstrating intrinsic mechanisms of protection rather than tissue-specific confounders. These findings reframe the somatic mutation hypothesis: organismal decline may not reflect total mutational burden, but where those mutations fall within the cellular network.

  PublisherDOI

• Surface optimization governs the local design of physical networks

  Nature · 2026-01-07 · 2 citations

  articleOpen accessSenior authorCorresponding

  The brain’s connectome1–3 and the vascular system4 are examples of physical networks whose tangible nature influences their structure, layout and, ultimately, their function. The material resources required to build and maintain these networks have inspired decades of research into wiring economy, offering testable predictions about their expected architecture and organization. Here we empirically explore the local branching geometry of a wide range of physical networks, uncovering systematic violations of the long-standing predictions of wiring minimization. This leads to the hypothesis that predicting the true material cost of physical networks requires us to account for their full three-dimensional geometry, resulting in a largely intractable optimization problem. We discover, however, an exact mapping of surface minimization onto high-dimensional Feynman diagrams in string theory5–7, predicting that, with increasing link thickness, a locally tree-like network undergoes a transition into configurations that can no longer be explained by length minimization. Specifically, surface minimization predicts the emergence of trifurcations and branching angles in excellent agreement with the local tree organization of physical networks across a wide range of application domains. Finally, we predict the existence of stable orthogonal sprouts, which are not only prevalent in real networks but also play a key functional role, improving synapse formation in the brain and nutrient access in plants and fungi. Analysis of the local branching geometries of several physical networks shows violations of predictions of length and volume minimization, leading to the hypothesis that estimating the material cost requires accounting for the full three-dimensional geometry.

  PublisherOA PDFDOI

• Human mobility in the metaverse mirrors patterns in the physical world

  Scientific Reports · 2026-04-04

  articleOpen accessSenior authorCorresponding

  The metaverse is a virtual space enabling interactions beyond geographical boundaries, promising to transform how people engage with each other both in the digital and the physical worlds. The lack of geographical boundaries and travel costs in the metaverse prompts us to ask if the fundamental laws that govern human mobility in the physical world apply. We collected data on avatar movements from Decentraland, along with their network mobility extracted from NFT purchases on Ethereum and Polygon. We find that despite the absence of mobility costs, an individual’s inclination to visit new locations diminishes over time, limiting movement to a small fraction of the metaverse. We also find a lack of correlation between land prices and visitation, a deviation from the patterns characterizing the physical world. Finally, we identify the scaling laws that characterize meta mobility and show that we need to add preferential selection to the existing models to explain quantitative patterns of metaverse mobility. Our ability to predict the characteristics of the emerging meta mobility network implies that the laws governing human mobility are rooted in fundamental patterns of human dynamics, rather than the nature of space and cost of movement.

  PublisherDOI

• Quantifying the impact of biobanks and cohort studies

  Proceedings of the National Academy of Sciences · 2025-04-16 · 5 citations

  articleOpen accessSenior authorCorresponding

  Biobanks advance biomedical and clinical research by collecting and offering data and biological samples for numerous studies. However, the impact of these repositories varies greatly due to differences in their purpose, scope, governance, and data collected. Here, we computationally identified 2,663 biobanks and their textual mentions in 228,761 scientific articles, 16,210 grants, 15,469 patents, 1,769 clinical trials, and 9,468 public policy documents, helping characterize the academic communities that utilize and support them. We found a strong concentration of biobank-related research on a few diseases, including obesity, Alzheimer's disease, breast cancer, and diabetes. Moreover, collaboration, rather than citation count, shapes the community's recognition of a biobank. We show that, on average, 41.1% of articles fail to reference any of the biobank's reference papers, but 59.6% include a biobank member as a coauthor. Using a generalized linear model, we identified the key factors that contribute to the impact of a biobank, finding that an impactful biobank tends to be more open to external researchers and that quality data-especially linked medical records-as opposed to large data, correlates with a higher impact in science, innovation, and disease. The collected data and findings are accessible through an open-access web application intended to inform strategies to expand access and maximize the value of these resources.

  PublisherOA PDFDOI

• Enhancing the Accuracy of Network Medicine Through Understanding the Impact of Sample Size in Gene Co-expression Networks

  Network and Systems Medicine · 2025-01-01 · 1 citations

  articleOpen accessSenior author

  Network medicine relies on RNA sequencing to infer gene co-expression networks, which are crucial to identify functional gene clusters and gene regulatory interactions, and offer a deeper understanding of disease phenotypes and drug mechanisms. It remains unknown, however, how many samples do we need to make reliable predictions. Here, we propose a power-law model to predict the relationship between the number of inferred significant interactions and sample size, allowing us to quantitatively link sample size to the accuracy of the inferred networks. We apply our model to investigate the effect of sample size on biomarker discovery and differentiation of protein–protein interactions from non-interacting pairs, ultimately unveiling the critical role of data quality in generating meaningful predictions in network medicine.

  PublisherOA PDFDOI

• Chemical Complexity of Food and Implications for Therapeutics

  New England Journal of Medicine · 2025-05-07 · 29 citations

  review
  PublisherDOI

• Human-AI Coevolution (Abstract Reprint)

  2025-09-01

  article

  Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.

  PublisherDOI

• Logarithmic kinetics and bundling in random packings of elongated 3D physical links

  Proceedings of the National Academy of Sciences · 2025-08-04 · 2 citations

  articleOpen accessSenior authorCorresponding

  We explore the impact of excluded volume interactions on the local assembly of linear physical networks, where nodes are spheres and links are rigid cylinders with varying length. To focus on the effect of elongated links, we introduce a minimal 3D model that helps us zoom into confined regions of these networks whose distant parts are sequentially connected by the random deposition of physical links with a very large aspect ratio. We show that the nonequilibrium kinetics at which these elongated links, or spaghetti, adhere to the available volume without mutual crossings is logarithmic in time, as opposed to the algebraic growth in lower dimensions for needle-like packings. We attribute this qualitatively different behavior to a delay in the activation of depletion forces caused by the 3D nature of the problem. Equally important, we find that this slow kinetics is metastable, allowing us to analytically predict the kinetic scaling characterizing an algebraic growth due to the nucleation of local bundles. Our findings offer a theoretical benchmark to study the local assembly of physical networks, with implications for the modeling of nest-like packings far from equilibrium.

  PublisherDOI

• Surface Optimisation Governs the Local Design of Physical Networks

  ArXiv.org · 2025-09-27

  preprintOpen accessSenior author

  The brain's connectome and the vascular system are examples of physical networks whose tangible nature influences their structure, layout, and ultimately their function. The material resources required to build and maintain these networks have inspired decades of research into wiring economy, offering testable predictions about their expected architecture and organisation. Here we empirically explore the local branching geometry of a wide range of physical networks, uncovering systematic violations of the long-standing predictions of length and volume minimisation. This leads to the hypothesis that predicting the true material cost of physical networks requires us to account for their full three-dimensional geometry, resulting in a largely intractable optimisation problem. We discover, however, an exact mapping of surface minimisation onto high-dimensional Feynman diagrams in string theory, predicting that with increasing link thickness, a locally tree-like network undergoes a transition into configurations that can no longer be explained by length minimisation. Specifically, surface minimisation predicts the emergence of trifurcations and branching angles in excellent agreement with the local tree organisation of physical networks across a wide range of application domains. Finally, we predict the existence of stable orthogonal sprouts, which not only are prevalent in real networks but also play a key functional role, improving synapse formation in the brain and nutrient access in plants and fungi.

  PublisherOA PDFDOI

Frequent coauthors

• Marc Vidal

  133 shared

• Amitabh Sharma

  126 shared

• Jörg Menche

  Austrian Academy of Sciences

  121 shared

• Yang‐Yu Liu

  Harvard University

  118 shared

• Joseph Loscalzo

  Harvard University

  107 shared

• Frederick P. Roth

  University of Pittsburgh

  96 shared

• Tong Hao

  83 shared

• Michael A. Calderwood

  75 shared

Education

• Ph.D., Physics

  Eötvös Loránd University

  1989

• M.S., Physics

  Eötvös Loránd University

  1984

• B.S., Physics

  Eötvös Loránd University

  1982

Awards & honors

• American Physical Society Julius Edgar Lilienfeld Prize (202…
• Fellow of AAAS
• Fellow of NAS