Albert-László Barabási

Albert-László Barabási (born March 30, 1967) is a Romanian-born

In 1989, Barabási emigrated to Hungary, together with his father. In 1991, he received a master's degree at Eötvös Loránd University in Budapest, under Tamás Vicsek, before enrolling in the Physics program at Boston University, where he earned a PhD in 1994. His thesis, written under the direction of H. Eugene Stanley,^[4] was published by Cambridge University Press under the title Fractal Concepts in Surface Growth.^[5][6]

Academic career

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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. In 2000, at the age of 32, he was named the Emil T. Hofman Professor of Physics, becoming the youngest endowed professor. In 2004 he founded the Center for Complex Network Research.

In 2005–06 he was a visiting professor at Harvard University. In fall 2007, Barabási left Notre Dame to become the distinguished professor and director of the Center for Network Science at Northeastern University and to take up an appointment in the department of medicine at Harvard Medical School.

As of 2008, Barabási holds Hungarian, Romanian and U.S. citizenship.^[7][8][9]

Research and achievements

Barabási has been a major contributor to the development of network science, the statistical physics of complex systems and network medicine.

Scale-Free Networks

He is best known for the discovery of the scale-free networks. He reported the scale-free nature of the WWW in 1999^[10] and the same year, in a Science paper with Réka Albert, he proposed the Barabási–Albert model, predicting that growth and preferential attachment are jointly responsible for the emergence of the scale-free property in real networks. According to the review of one of Barabási's books, preferential attachment can be described as follows:

Barabási has found that the websites that form the network (of the WWW) have certain mathematical properties. The conditions for these properties to occur are threefold. The first is that the network has to be expanding, growing. This precondition of growth is very important as the idea of emergence comes with it. It is constantly evolving and adapting. That condition exists markedly with the world wide web. The second is the condition of preferential attachment, that is, nodes (websites) will wish to link themselves to hubs (websites) with the most connections. The third condition is what is termed competitive fitness which in network terms means its rate of attraction.^[11]

He subsequently showed that the scale-free property emerges in biological systems, namely in

In a 2001 paper with Réka Albert and Hawoong Jeong he demonstrated the Achilles' heel property of scale-free networks, showing that such networks are robust to random failures but fragile to attacks.^[16] Specifically, they showed that networks can easily survive the random failure of a very large number of nodes, showing a remarkable robustness to failures. At the same time, networks can quickly collapse under attack, achieved by removing the biggest hubs. The threshold characterizing the breakdown of a network under random failures was linked^[17] to the second moment of the degree distribution, whose convergence to zero for large networks explain why heterogenous networks can survive the failure of a large fraction of their nodes. The calculations also showed that robustness to random failures is not limited to scale-free networks, but it is a general property of most real networks with a wide range of node degrees.

Network Medicine

Barabási is one of the founders of network medicine, a term he coined in an article entitled "Network Medicine – From Obesity to the "Diseasome", published in The New England Journal of Medicine, in 2007.^[18] His work introduced the concept of diseasome, or disease network,^[19] showing that diseases are connected through shared genes, capturing their common genetic roots. He subsequently pioneered the use of large patient data, linking the roots of disease comorbidity to molecular networks.^[20] A key concept of network medicine is Barabási's discovery that genes associated with the same disease are located in the same network neighborhood,^[21] which led to the concept of disease module, currently used to aid drug discovery, drug design, and the development of biomarkers, as he outlined in 2012 in a TEDMED talk.^[22] Barabási's work has led to the founding of the Channing Division of Network Medicine at Harvard Medical School and the Network Medicine Institute, representing 33 universities and institutions around the world committed to advancing the field. Barabási's work in network medicine has led to multiple experimentally falsifiable predictions, helping identify experimentally validated novel pathways in asthma,^[23] predicting a novel mechanism of action for rosmarinic acid,^[24] and novel therapeutic functions of existing drugs (drug repurposing).^[25] The products of network medicine have reached the clinic, helping doctors decide if rheumatoid arthritis patients respond to anti-TNF therapy.^[26][27] During COVID  Barabási led a major collaboration involving researchers from Harvard University, Boston University and The Broad Institute, to predict and experimentally test the efficacy for COVID patients of 6,000 approved drugs.^[28][29]

Human Dynamics

Barabási in 2005 discovered the fat tailed nature of the inter event times in human activity patterns. The pattern indicated that human activity is bursty - short periods of intensive activity are followed by long periods that lack detectable activity. Such bursty patterns have been subsequently discovered in a wide range of processes, from web browsing to email communications. He proposed the Barabási model^[30] of human dynamics, demonstrating that a queuing model can explain the bursty nature of human activity, a topic is covered by his book Bursts: The Hidden Pattern Behind Everything We Do.^[31]

Human Mobility

Barabási laid foundational work in understanding individual human mobility patterns through a series of influential papers. In his 2008 Nature publication,^[32] Barabási utilized anonymized mobile phone data to analyze human mobility, challenging the existing random walk models. He discovered that human movements exhibit 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,^[33] Barabási explored the predictability of human dynamics by analyzing mobile phone user trajectories. Contrary to expectations, he found a high predictability of 93% in human movements across all users, with individual predictability rarely dropping below 80%. He then introduced two principles underlying human trajectories, leading to the development of a microscopic model for individual mobility.^[34] This model, consistent with observed empirical scaling laws, provided analytical insights into key mobility pattern features. Remarkably, a decade before the COVID-19 pandemic, Barabási was able to predict the spreading patterns of a virus transmitted through direct contact.^[35]

Network Control

His work on network controllability and observability asked the fundamental question of how large networks control themselves. To answer this, he was the first to bring the tools of control theory to network science. His first work proposed a method to identify the nodes through which one can control a complex network, just like a car is controlled through three control points, the steering wheel, gas pedal and brake. He proposed the formalism of network controllability by mapping the control problem, widely studied in physics and engineering since Maxwell, into graph matching, a graph theoretic problem, merging statistical mechanics and control theory.^[36] The exact mapping allowed him to develop tools to identify the system's control nodes. He used network control to predict the function of individual neurons in the Caenorhabditis elegans connectome, leading to the discovery of new neurons involved in the control of locomotion, and offering direct falsifiable experimental confirmation of network control principles.^[37]

Critique

In 2014, Lior Pachter and his student Nicolas Bray published a three-part analysis of what they deemed questionable papers in computational biology, including some of Barabási's work. They argued that Barabási has an undeserved reputation for brilliance, because Barabási publicizes his work far more intensely than his critics disseminate their refutations. Pachter and Bray provide a small list of examples, in which Barabási's work was subsequently analyzed to be trivially refutable after publication.^[38]

Outside computational biology, critiques have identified various flaws in the methodology of Barabási's articles on the scale-freeness of the Web,^[39][40] the ubiquity of power-laws in general,^[41] and the ubiquity of scale-free networks more specifically,^[42] his theories on network control^[43] and the dynamics of human activities.^[44][45] Barabási has also been particularly criticised for failing to acknowledge the contribution of Derek de Solla Price to the scale-free network concept, whose model of citation networks predated the BA model.^[46] The BA model is an undirected version of the Price model, although many properties of the two models do not depend on the directedness of edges.^[47]

Awards

Barabási was the recipient of the 2023

In 2021 Barabási received the EPS Statistical and Nonlinear Physics Prize, for "his pioneering contributions to the development of complex network science, in particular for his seminal work on scale-free networks, the preferential attachment model, error and attack tolerance in complex networks, controllability of complex networks, the physics of social ties, communities, and human mobility patterns, genetic, metabolic, and biochemical networks, as well as applications in network biology and network medicine."

In 2021 Barabási was ranked 2nd in the world in the field of Engineering and Technology.^[49]

In 2019 he received The Bolyai Prize from the Hungarian Academy of Sciences and in 2017 he received the Senior Scientific Award of the Complex Systems Society for "setting the basis of what is now modern Network Science".^[50]

In 2011 he received the

In 2006 he was awarded the John von Neumann Medal by the John von Neumann Computer Society from Hungary, for outstanding achievements in computer-related science and technology.^[53]

In 2005, he was awarded the FEBS Anniversary Prize for Systems Biology.

Barabási was elected a

In 2004, he was elected as an external member of the Hungarian Academy of Sciences, in 2007, he was inducted into the Academia Europaea, ^[55] in 2013 he was elected as fellow of the Massachusetts Academy of Sciences, in 2018 he was elected into the European Academy of Arts and Sciences, and in 2018 was elected member of the Romanian Academy of Sciences.^[56]

In 2023 he was awarded a Doctor Honoris Causa by Obuda University in Hungary, in 2011 by the Technical University of Madrid,^[57] in 2018 by Utrecht University^[58] and University of West Timisoara.^[59]

Selected publications

• Barabási, Albert-László, The Formula: The Universal Laws of Success, November 6, 2018;
  ISBN 0-316-50549-8
  (hardcover)
• Barabási, Albert-László (2018). Network science. Cambridge University Press.
  ISBN 978-1107076266
  .
• Barabási, Albert-László, Bursts: The Hidden Pattern Behind Everything We Do, April 29, 2010;
  ISBN 0-525-95160-1
  (hardcover)
• Barabási, Albert-László,
  ISBN 0-452-28439-2
  (pbk)
• Barabási, Albert-László and Réka Albert, "Emergence of scaling in random networks", Science, 286:509–512, October 15, 1999
• Barabási, Albert-László and Zoltán Oltvai, "Network Biology", Nature Reviews Genetics 5, 101–113 (2004)
• Barabási, Albert-László,
  ISBN 0-691-11357-2
• Barabási, Albert-László, Natali Gulbahce, and Joseph Loscalzo, "Network Medicine", Nature Reviews Genetics 12, 56–68 (2011)
• Réka Albert, Hawoong Jeong & Barabási, Albert-László (1999). "The Diameter of the WWW". Nature. 401 (6749): 130–31.
  S2CID 4419938
  .
• Y.-Y. Liu, J.-J. Slotine, A.-L. Barabási, "Controllability of complex networks", Nature 473, 167–173 (2011)
• Y.-Y. Liu, J.-J. Slotine, A.-L. Barabási, "Observability of complex systems", Proceedings of the National Academy of Sciences 110, 1–6 (2013)
• Baruch Barzel and A.-L. Barabási, "Universality in Network Dynamics", Nature Physics 9, 673–681 (2013)
• Baruch Barzel and A.-L. Barabási, "Network link prediction by global silencing of indirect correlations", Nature Biotechnology 31, 720–725 (2013)
• B. Barzel Y.-Y. Liu and A.-L. Barabási, "Constructing minimal models for complex system dynamics", Nature Communications 6, 7186 (2015)