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Albert-László Barabási

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The native form of this personal name is Barabási Albert-László. This article uses Western name order when mentioning individuals.
Albert-László Barabási
Barabási at the World Economic Forum Annual Meeting of the New Champions in 2012
Born
Barabási Albert László

(1967-03-30) March 30, 1967 (age 57)
Cârța, Harghita County, Romania
Citizenship
Romanian
Hungarian
American
Alma materUniversity of Bucharest
Eötvös Loránd University (MS)
Boston University (PhD)
Known forResearch of network science
The concept of scale-free networks
Proposal of Barabási–Albert model
Founder of Network Medicine
Introducing Network controllability
Awards
Scientific career
FieldsPhysics, Network Science, Network Medicine
ThesisGrowth and roughening of non-equilibrium interfaces (1994)
Doctoral advisorH. Eugene Stanley
Doctoral students
Websitebarabasilab.com

Albert-László Barabási (born March 30, 1967) is a Romanian-born Hungarian-American physicist, best known for his discoveries in network science and network medicine.

He is a distinguished university professor and Robert Gray Professor of Network Science at Northeastern University, and holds appointments at the department of medicine, Harvard Medical School and the department of network and data science[1] at Central European University. He is the former Emil T. Hofmann Professor of Physics at the University of Notre Dame and former associate member of the Center of Cancer Systems Biology (CCSB) at the Dana–Farber Cancer Institute, Harvard University.

He discovered in 1999 the concept of scale-free networks and proposed the Barabási–Albert model to explain their widespread emergence in natural, technological and social systems, from the cellular telephone to the World Wide Web or online communities. He is the founding president of the Network Science Society,[2] which sponsors the flagship NetSci Conference series held since 2006.

Birth and education

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Barabási was born to an ethnic Hungarian family in Cârța, Harghita County, Romania. His father, László Barabási, was a historian, museum director and writer, while his mother, Katalin Keresztes, taught literature, and later became director of a children's theater.[3] He attended a high school specializing in science and mathematics; in the tenth grade, he won a local physics olympiad. Between 1986 and 1989, he studied physics and engineering at the University of Bucharest; during that time, he began doing research on chaos theory, publishing three papers.[3]

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 2005–6 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

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Barabási contributors to network science and network medicine have fundamentally changed the study of complex systems.

Scale-Free Networks

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He is best known for the 1999 discovery of scale-free networks, after creating a map of the WWW in 1999[10] and realizing that the degree distribution does not follow the Poisson distribution expected for random networks, but rather follows a power law. 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. In the next year he showed that the power law degree distribution is not limited to the WWW, but also emerges in metabolic networks[11] and protein–protein interaction[12] networks, demonstrating the universality of the scale-free property. Science celebrated the ten-year anniversary of Barabási’s 1999 discovery of scale-free networks, one of the most cited Science papers of all times, by devoting a special issue to Complex Systems and Networks in 2009.[13][14]

Network Robustness and Resilience

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In a 2001 paper with Réka Albert and Hawoong Jeong, he showed that networks are robust to random failures but fragile to attacks,[15] known as the Achilles' heel property of scale-free networks. Specifically, networks can easily survive the random failure of a very large number of nodes, demonstrating a remarkable robustness to failures. At the same time, networks quickly collapse under attack, achieved by removing the biggest hubs. The breakdown threshold of a network was linked[16] 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. In 2016, he extended this concepts to resilience,[17] showing that the network structure determines a system's ability to display resilience. While robustness refers to the system's ability to carry out its basic functions even when some of its nodes and links may be missing, a system is resilient if it can adapt to internal and external errors by changing its mode of operation, without losing its ability to function. Hence resilience is a dynamical property that requires a shift in the system's core activities.

Network Medicine

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Barabási is one of the founders of network medicine,[18] 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.[19] His work introduced the concept of diseasome, or disease network,[20] 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.[21] A key concept of network medicine is Barabási's discovery that genes associated with the same disease are located in the same network neighborhood,[22] 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.[23] 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,[24] predicting a novel mechanism of action for rosmarinic acid,[25] and novel therapeutic functions of existing drugs (drug repurposing).[26] The products of network medicine have reached the clinic, helping doctors decide if rheumatoid arthritis patients respond to anti-TNF therapy.[27][28] 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.[29][30]

Human Dynamics

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Barabási in 2005 discovered the fat tailed nature of the interevent 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. Bursty patterns have been subsequently discovered in a wide range of processes, from web browsing to email communications and gene expression patterns. He proposed the Barabási model[31] of human dynamics, to explain the phenomena, 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.[32]

Human Mobility

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Barabási laid foundational work in understanding individual human mobility patterns through a series of influential papers. In his 2008 Nature publication,[33] 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,[34] he explored the predictability of human dynamics by analyzing mobile phone user trajectories. Contrary to expectations, he found a 93% predictability of in human movements across all users. He introduced two principles governing human trajectories, leading to the development of the widely used model for individual mobility.[35] Using this modeling framework, a decade before the COVID-19 pandemic, Barabási predicted the spreading patterns of a virus transmitted through direct contact.[36]

Network Control

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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. He proposed a method to identify the nodes through which one can control a complex network, by mapping the control problem, widely studied in physics and engineering since Maxwell, into graph matching, merging statistical mechanics and control theory.[37] He used network control to predict the function of individual neurons in the Caenorhabditis elegans connectome, offering direct experimental confirmation of network control principles by discovering new neurons involved in locomotion.[38]

Awards

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Barabási was the recipient of the 2024 Gothenburg Lise Meitner Award[39]; he has also been the recipient of the 2023 Julius Edgar Lilienfeld Prize, the top prize of the American Physical Society,[40] "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." In 2021 he received the EPS Statistical and Nonlinear Physics Prize, awarded by the European Physical Society 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."

Barabási has been elected to the US National Academy of Sciences (2024),[41] Austrian Academy of Sciences (2024), Hungarian Academy of Sciences (2004), Academia Europaea (2007), [42] European Academy of Sciences and Art (2018), Romanian Academy of Sciences[43] (2018) and the Massachusetts Academy of Sciences (2013). He was elected Fellow of the American Physical Society (2003),[44] of the American Association for the Advancement of Science (2011), of the Network Science Society (2021). He was awarded a Doctor Honoris Causa by Obuda University (2023) in Hungary, the Technical University of Madrid[45] (2011), Utrecht University[46] (2018) and West University of Timișoara (2020).[47]

He received the Bolyai Prize from the Hungarian Academy of Sciences (2019), the Senior Scientific Award of the Complex Systems Society (2017) for "setting the basis of what is now modern Network Science",[48] the Lagrange Prize (2011) C&C Prize (2008) Japan "for stimulating innovative research on networks and discovering that the scale-free property is a common feature of various real-world complex networks"[49] and the Cozzarelli Prize, National Academies of Sciences (USA),[50] John von Neumann Medal (2006) awarded by the John von Neumann Computer Society from Hungary, for outstanding achievements in computer-related science and technology[51] and the FEBS Anniversary Prize for Systems Biology (2005).

In 2021, Barabási was ranked 2nd in the world in the field of Engineering and Technology.[52]

Selected publications

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  • 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, Linked: The New Science of Networks, 2002. 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, Mark Newman and Duncan J. Watts, The Structure and Dynamics of Networks, 2006; 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. arXiv:cond-mat/9907038. Bibcode:1999Natur.401..130A. doi:10.1038/43601. 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).
  • J. Gao, B. Barzel, A.-L, Barabási, "Universal resilience patterns in complex networks". Nature 530(7590):307-12 (2016).

References

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  2. ^ "NetSci – the Network Science Society".
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  5. ^ Albert-László Barabási at the Mathematics Genealogy Project
  6. ^ Albert-Laszlo Barabasi, Eugene H Stanley (1995). Fractal Concepts in Surface Growth. Cambridge University Press. ISBN 9780511599798.
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  10. ^ Albert, Réka; Jeong, Hawoong; Barabási, Albert-László (September 1999). "Diameter of the World-Wide Web". Nature. 401 (6749): 130–131. arXiv:cond-mat/9907038. Bibcode:1999Natur.401..130A. doi:10.1038/43601. S2CID 4419938.
  11. ^ Jeong, H.; Tombor, B.; Albert, R.; Oltvai, Z. N.; Barabási, A.-L. (October 2000). "The large-scale organization of metabolic networks". Nature. 407 (6804): 651–654. arXiv:cond-mat/0010278. Bibcode:2000Natur.407..651J. doi:10.1038/35036627. PMID 11034217.
  12. ^ Jeong, H.; Mason, S. P.; Barabási, A.-L.; Oltvai, Z. N. (May 2001). "Lethality and centrality in protein networks". Nature. 411 (6833): 41–42. arXiv:cond-mat/0105306. Bibcode:2001Natur.411...41J. doi:10.1038/35075138. PMID 11333967.
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  17. ^ J. Gao, B. Barzel, A.-L. Barabasi (2016). "Universal resilience patterns in complex networks". Nature. 530 (7590): 307–312. doi:10.1038/nature16948. PMID 26887493.{{cite journal}}: CS1 maint: multiple names: authors list (link)
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  19. ^ Barabási, Albert-László (July 26, 2007). "Network Medicine — From Obesity to the 'Diseasome'". New England Journal of Medicine. 357 (4): 404–407. doi:10.1056/NEJMe078114. PMID 17652657.
  20. ^ Goh, Kwang-Il; Cusick, Michael E.; Valle, David; Childs, Barton; Vidal, Marc; Barabási, Albert-László (2007). "The human disease network". Proceedings of the National Academy of Sciences. 104 (21): 8685–8690. Bibcode:2007PNAS..104.8685G. doi:10.1073/pnas.0701361104. PMC 1885563. PMID 17502601.
  21. ^ Barabási, Albert-László; Gulbahce, Natali; Loscalzo, Joseph (January 2011). "Network medicine: a network-based approach to human disease". Nature Reviews Genetics. 12 (1): 56–68. doi:10.1038/nrg2918. PMC 3140052. PMID 21164525.
  22. ^ Menche, J.; Sharma, A.; Kitsak, M.; Ghiassian, S. D.; Vidal, M.; Loscalzo, J.; Barabasi, A.-L. (February 20, 2015). "Uncovering disease-disease relationships through the incomplete interactome". Science. 347 (6224): 1257601. doi:10.1126/science.1257601. PMC 4435741. PMID 25700523.
  23. ^ Do your proteins have their own social network?, May 31, 2012, retrieved November 1, 2022
  24. ^ Sharma, Amitabh; Menche, Jörg; Huang, C. Chris; Ort, Tatiana; Zhou, Xiaobo; Kitsak, Maksim; Sahni, Nidhi; Thibault, Derek; Voung, Linh; Guo, Feng; Ghiassian, Susan Dina; Gulbahce, Natali; Baribaud, Frédéric; Tocker, Joel; Dobrin, Radu; Barnathan, Elliot; Liu, Hao; Panettieri, Reynold A.; Tantisira, Kelan G.; Qiu, Weiliang; Raby, Benjamin A.; Silverman, Edwin K.; Vidal, Marc; Weiss, Scott T.; Barabási, Albert-László (June 2015). "A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma". Human Molecular Genetics. 24 (11): 3005–3020. doi:10.1093/hmg/ddv001. PMC 4447811. PMID 25586491.
  25. ^ do Valle, Italo F.; Roweth, Harvey G.; Malloy, Michael W.; Moco, Sofia; Barron, Denis; Battinelli, Elisabeth; Loscalzo, Joseph; Barabási, Albert-László (March 19, 2021). "Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols". Nature Food. 2 (3): 143–155. doi:10.1038/s43016-021-00243-7. hdl:1871.1/f3838307-b0e0-4c44-91e0-0aca528010c1. PMID 37117448. S2CID 232317723.
  26. ^ Cheng, Feixiong; Desai, Rishi J.; Handy, Diane E.; Wang, Ruisheng; Schneeweiss, Sebastian; Barabási, Albert-László; Loscalzo, Joseph (July 12, 2018). "Network-based approach to prediction and population-based validation of in silico drug repurposing". Nature Communications. 9 (1): 2691. Bibcode:2018NatCo...9.2691C. doi:10.1038/s41467-018-05116-5. PMC 6043492. PMID 30002366.
  27. ^ Cohen, Stanley; Wells, Alvin F.; Curtis, Jeffrey R.; Dhar, Rajat; Mellors, Theodore; Zhang, Lixia; Withers, Johanna B.; Jones, Alex; Ghiassian, Susan D.; Wang, Mengran; Connolly-Strong, Erin; Rapisardo, Sarah; Gatalica, Zoran; Pappas, Dimitrios A.; Kremer, Joel M.; Saleh, Alif; Akmaev, Viatcheslav R. (September 2021). "A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study". Rheumatology and Therapy. 8 (3): 1159–1176. doi:10.1007/s40744-021-00330-y. PMC 8214458. PMID 34148193.
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  32. ^ Barabási, Albert-László (2010). Bursts : the hidden pattern behind everything we do. New York, N.Y.: Dutton. ISBN 978-0-525-95160-5. OCLC 426800811.[page needed]
  33. ^ González, Marta C.; Hidalgo, César A.; Barabási, Albert-László (June 2008). "Understanding individual human mobility patterns". Nature. 453 (7196): 779–782. arXiv:0806.1256. Bibcode:2008Natur.453..779G. doi:10.1038/nature06958. PMID 18528393. S2CID 4419468.
  34. ^ Song, Chaoming; Qu, Zehui; Blumm, Nicholas; Barabási, Albert-László (February 19, 2010). "Limits of Predictability in Human Mobility". Science. 327 (5968): 1018–1021. Bibcode:2010Sci...327.1018S. doi:10.1126/science.1177170. PMID 20167789. S2CID 13847306.
  35. ^ Song, Chaoming; Koren, Tal; Wang, Pu; Barabási, Albert-László (October 2010). "Modelling the scaling properties of human mobility". Nature Physics. 6 (10): 818–823. arXiv:1010.0436. Bibcode:2010NatPh...6..818S. doi:10.1038/nphys1760.
  36. ^ Wang, Pu; González, Marta C.; Hidalgo, César A.; Barabási, Albert-László (May 22, 2009). "Understanding the Spreading Patterns of Mobile Phone Viruses". Science. 324 (5930): 1071–1076. arXiv:0906.4567. Bibcode:2009Sci...324.1071W. doi:10.1126/science.1167053. PMID 19342553.
  37. ^ Liu, Yang-Yu; Slotine, Jean-Jacques; Barabási, Albert-László (2011). "Controllability of complex networks". Nature. 473 (7346): 167–173. Bibcode:2011Natur.473..167L. doi:10.1038/nature10011. PMID 21562557. S2CID 4334171.
  38. ^ Yan, Gang; Vértes, Petra E.; Towlson, Emma K.; Chew, Yee Lian; Walker, Denise S.; Schafer, William R.; Barabási, Albert-László (October 2017). "Network control principles predict neuron function in the Caenorhabditis elegans connectome". Nature. 550 (7677): 519–523. Bibcode:2017Natur.550..519Y. doi:10.1038/nature24056. PMC 5710776. PMID 29045391.
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