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==Research and achievements==
==Research and achievements==
Barabási has been a major contributor to the development of [[network science]] and the statistical physics of complex systems.
⚫
Barabási has been a major contributor to the development of [[network science]] and the statistical physics of complex systems. His biggest role has been the discovery of the ''[[scale-free network]]s''. He reported the scale-free nature of the WWW in 1999 and the same year, in a Science paper with Réka [[Réka Albert|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:<blockquote>"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."<ref>[http://www.sociopranos.com/bookreviewlinked.htm Profile] {{webarchive|url=https://web.archive.org/web/20050309024313/http://www.sociopranos.com/bookreviewlinked.htm |date=March 9, 2005 }}, sociopranos.com; accessed 10 January 2016.</ref></blockquote>

=== Scale-Free Networks ===
⚫
His biggest role has been the discovery of the ''[[scale-free network]]s''. He reported the scale-free nature of the WWW in 1999 and the same year, in a Science paper with Réka [[Réka Albert|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:<blockquote>"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."<ref>[http://www.sociopranos.com/bookreviewlinked.htm Profile] {{webarchive|url=https://web.archive.org/web/20050309024313/http://www.sociopranos.com/bookreviewlinked.htm |date=March 9, 2005 }}, sociopranos.com; accessed 10 January 2016.</ref></blockquote>


He subsequently showed that the scale-free property emerges in biological systems, namely in [[metabolic networks]] and [[protein–protein interaction]] networks. [[Science (journal)|''Science'']] celebrated the ten-year anniversary of Barabási’s 1999 discovery by devoting a special issue to Complex Systems and Networks in 2009.<ref>{{Cite journal |last=Barabasi |first=Albert-Laszlo |date=2009 |title=Scale-Free Networks: A Decade and Beyond |url=https://www.science.org/doi/10.1126/science.1173299 |journal=Science |volume=325 |issue=5939 |pages=412-413}}</ref><ref>{{Cite journal |last=Jasny |first=Barbara |date=2009 |title=Connections |url=https://www.science.org/doi/10.1126/science.325_405 |journal=Science |volume=325 |issue=5939 |pages=405}}</ref>
He subsequently showed that the scale-free property emerges in biological systems, namely in [[metabolic networks]] and [[protein–protein interaction]] networks. [[Science (journal)|''Science'']] celebrated the ten-year anniversary of Barabási’s 1999 discovery by devoting a special issue to Complex Systems and Networks in 2009.<ref>{{Cite journal |last=Barabasi |first=Albert-Laszlo |date=2009 |title=Scale-Free Networks: A Decade and Beyond |url=https://www.science.org/doi/10.1126/science.1173299 |journal=Science |volume=325 |issue=5939 |pages=412-413}}</ref><ref>{{Cite journal |last=Jasny |first=Barbara |date=2009 |title=Connections |url=https://www.science.org/doi/10.1126/science.325_405 |journal=Science |volume=325 |issue=5939 |pages=405}}</ref>


=== Network Robustness ===
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.<ref>{{cite book |last1=Barabási |first1=Albert-László |title=Network science |date=July 21, 2016 |location=Cambridge, United Kingdom |isbn=9781107076266}}</ref> The implications of this robustness and fragility also extend to many infrastructure systems, though it has been argued that the internet's resilience is not related to the scale-free structure.<ref>{{Cite journal| mr=2509062|last1=Willinger|first1=Walter|last2=Alderson|first2=David|last3=Doyle|first3=John C|title=Mathematics and the Internet: a source of enormous confusion and great potential| journal=[[Notices of the American Mathematical Society]]| volume=56|year=2009|issue=5|pages=586–599| url=https://www.ams.org/notices/200905/rtx090500586p.pdf}}</ref><ref>{{Cite journal|last1=Adamic|first1=Lada A.|last2=Huberman|first2=Bernardo A.|date=2000-01-31|title=Comment to "Emergence of Scaling in Random Networks" (cond-mat/9910332)|arxiv=cond-mat/0001459|bibcode=2000cond.mat..1459A|language=en}}</ref> Some have pushed back on whether power-laws are as ubiquitous as suggested and noted that power-laws with exponential cutoffs or lognormal distributions might be better descriptors of many real datasets.<ref>{{Cite journal |arxiv = 0706.1062|doi = 10.1137/070710111|bibcode = 2009SIAMR..51..661C|title = Power-Law Distributions in Empirical Data|journal = SIAM Review|volume = 51|issue = 4|pages = 661–703|year = 2009|last1 = Clauset|first1 = Aaron|last2 = Shalizi|first2 = Cosma Rohilla|last3 = Newman|first3 = M. E. J.|s2cid = 9155618}}</ref><ref>{{Cite journal |arxiv = 1801.03400|doi = 10.1038/s41467-019-08746-5|pmid = 30833554|pmc = 6399239|bibcode = 2019NatCo..10.1017B|title = Scale-free networks are rare|journal = Nature Communications|volume = 10|pages = 1017|year = 2019 |last1 = Broido|first1 = Anna D.|last2 = Clauset|first2 = Aaron|issue = 1}}</ref><ref>{{Cite arXiv |eprint = physics/0510216|last1 = Barabási|first1 = Albert-László|last2 = Malmgren|first2 = R. Dean|last3 = Amaral|first3 = Luis A. N.|title = Comment on Barabasi, Nature 435, 207 (2005)|year = 2005}}</ref> Following this debate, Serafino et al. used a finite size scaling analysis to reaffirm the universality of power-laws over a large ensemble of real-world networks. <ref>{{Cite journal | doi=10.1073/pnas.2013825118| title=True scale-free networks hidden by finite size effects| journal=PNAS| volume=118| issue=2| pages= e2013825118 | year=2021| last1=Serafino| first1=Matteo| last2=Cimini| first2=Giulio| last3=Maritan| first3=Amos| last4=Rinaldo| first4=Andrea| last5=Suweis| first5=Samir | last6=Banavar| first6=Jayanth R. | last7=Caldarelli| first7=Guido| pmid=33380456| pmc=7812829| doi-access=free}}</ref>
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.<ref>{{cite book |last1=Barabási |first1=Albert-László |title=Network science |date=July 21, 2016 |location=Cambridge, United Kingdom |isbn=9781107076266}}</ref> Specifically, they showed that networks can easily survive the random failure of a very large number of nodes, i.e. the failure threshold is high. In contrast, the same networks collapse of they are attacked, by removing the biggest hubs first. The threshold characterizing the breakdown of a network under random failures was later analytically derived<ref>{{Cite journal |last=Cohen, Reuven; Erez, Keren; ben-Avraham, Daniel; Havlin, Shlomo |date=2000 |title="Resilience of the Internet to Random Breakdowns". |url=https://arxiv.org/abs/cond-mat/0007048 |journal=Physical Review Letters |volume=85 |issue=21 |pages=4626–4628}}</ref> , linking it to the second moment of the [[degree distribution]]. The threshold converges to zero for large networks, indicating that large networks can easily survive the failure of a very large fraction of their nodes. The calculations also showed that robustness to random failures is not limited to scale-free networks, but it a general property of most real networks with a wide range of node degrees.


=== Network Medicine ===
Barabási's contributions to network biology and [[network medicine]] include introducing the concept of diseasome, or disease network, showing how diseases link to each other through shared genes and pioneered the use of large patient data to explore disease comorbidity, linking it to molecular network data.<ref>{{cite journal |last1=Barabási |first1=Albert-László |last2=Gulbahce |first2=Natali |last3=Loscalzo |first3=Joseph |title=Network medicine: a network-based approach to human disease |journal=Nature Reviews Genetics |date=January 2011 |volume=12 |issue=1 |pages=56–68 |doi=10.1038/nrg2918|pmid=21164525 |pmc=3140052 }}</ref> Nicholas Bray and [[Lior Pachter]] criticized some of Barabási's work in computational biology in a blog post,<ref>{{cite web|url=https://liorpachter.wordpress.com/2014/02/10/the-network-nonsense-of-albert-laszlo-barabasi/|title =The network nonsense of Albert-László Barabási|access-date=2018-05-11|date =February 10, 2014}}</ref> attacking the entire field of network science as part of a series against several leading researchers.<ref>{{cite web|url=https://liorpachter.wordpress.com/2014/02/11/the-network-nonsense-of-manolis-kellis/|title =The network nonsense of Manolis Kellis|access-date=2018-05-11|date =February 11, 2014}}</ref>
Barabási is one of the founders of [[network medicine]], coined and popularized in a scientific article called "Network Medicine – From Obesity to the "Diseasome", published in The New England Journal of Medicine, in 2007<ref>{{Cite journal |last=Barabási AL |title=Network medicine--from obesity to the "diseasome" |journal=N Engl J Med. |volume=357 |issue=4 |pages=404-407}}</ref>. His work introduced the concept of diseasome, or disease network<ref>{{Cite journal |last=Goh, K. I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A. L. |date=2007 |title=The human disease network |journal=Proceedings of the National Academy of Sciences |volume=104 |issue=21 |pages=8685-8690}}</ref>, showing how diseases link to each other through shared genes, capturing their common genetic roots. He subsequently pioneered the use of large patient data to explore disease comorbidity, linking it to molecular network data.<ref>{{cite journal |last1=Barabási |first1=Albert-László |last2=Gulbahce |first2=Natali |last3=Loscalzo |first3=Joseph |title=Network medicine: a network-based approach to human disease |journal=Nature Reviews Genetics |date=January 2011 |volume=12 |issue=1 |pages=56–68 |doi=10.1038/nrg2918|pmid=21164525 |pmc=3140052 }}</ref> He subsequently discovered that genes associated with the same disease tend to be located in the same network neighborhood<ref>{{Cite journal |last=Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barabási AL. |date=2005 |title=Uncovering disease-disease relationships through the incomplete interactome. |journal=Science |volume=347 |issue=6224 |pages=1257601}}</ref>, introducing the concept of disease module, currently used to aid [[drug discovery]], [[drug design]], and the development of [[Biomarker|biomarkers]] for disease detection. His work has led to the founding of the [https://www.brighamandwomens.org/research/departments/channing-division-of-network-medicine/overview Channing Division of Network Medicine at Harvard Medical School] and to the founding of the [https://www.network-medicine.org/ Network Medicine Institute and Global Alliance], representing 33 leading universities and institutions around the world committed to advancing the field of Network Medicine. Network medicine has led to multiple experimentally falsifiable predictions, successfully tested, providing experimentally validated novel pathways in asthma, predicting novel mechanism of action for rosmarinic acid, and predicting novel therapeutic functions of existing drugs, and testing them directly in patients (drug repurposing). During COVID <sup> </sup>Barabási has led a major collaboration involving researchers at [[Harvard University]], [[Boston University]] and Broad Institute, predicting and experimentally testing the potential efficacy for COVID patients of 6,000 approved drugs<ref>{{Cite journal |last=Morselli Gysi, D., Do Valle, Í., Zitnik, M., Ameli, A., Gan, X., Varol, O., Ghiassian, S.D., Patten, J.J., Davey, R.A., Loscalzo, J. and Barabási, A.L. |date=2021 |title=Network medicine framework for identifying drug-repurposing opportunities for COVID-19. |journal=Proceedings of the National Academy of Sciences |volume=118 |issue=19 |pages=e2025581118}}</ref>.


=== Human Dynamics ===
His work on [[human dynamics]] resulted in the discovery of the fat tailed nature of the inter event times in human activity patterns, and proposed the Barabási model<ref name="Barabasi05-1">{{cite journal|author=A.-L. Barabási|year=2005|title=The origin of bursts and heavy tails in human dynamics|journal=Nature|volume=435|pages=207–11|pmid=15889093|issue=7039|doi=10.1038/nature03459|arxiv = cond-mat/0505371|bibcode=2005Natur.435..207B|s2cid=4419475}}</ref> that showed that a queuing model was capable of explaining the bursty nature of human activity. This topic is covered by his book Bursts.{{citation needed|date=January 2016}}
His work on [[human dynamics]] resulted in the discovery of the fat tailed nature of the inter event times in human activity patterns, and proposed the Barabási model<ref name="Barabasi05-1">{{cite journal|author=A.-L. Barabási|year=2005|title=The origin of bursts and heavy tails in human dynamics|journal=Nature|volume=435|pages=207–11|pmid=15889093|issue=7039|doi=10.1038/nature03459|arxiv = cond-mat/0505371|bibcode=2005Natur.435..207B|s2cid=4419475}}</ref> that showed that a queuing model was capable of explaining the bursty nature of human activity. This topic is covered by his book Bursts.{{citation needed|date=January 2016}}


=== Network Control ===
His work on network control and [[observability]] brought the tools of [[control theory]] to network science. It asked how to identify the nodes from which one can control a complex network, just like a car is controlled through three control points, the steering wheel, gas pedal and the brake. By establishing an exact mapping between the dynamical control problem and [[matching (graph theory)|matching]] theory, he developed tools to identify the system's control nodes. The same mapping allowed the determination of observers, nodes whose state allows one to reconstruct the state of the full system.{{citation needed|date=January 2016}} However, further work disproved this notion, showing that not observer or control nodes, but nodal dynamics determines how networks could be controlled.<ref>{{Cite journal | doi=10.1371/journal.pone.0038398| pmid=22761682| pmc=3382243| title=Nodal Dynamics, Not Degree Distributions, Determine the Structural Controllability of Complex Networks| journal=PLOS ONE| volume=7| issue=6| pages=e38398| year=2012| last1=Cowan| first1=Noah J.| last2=Chastain| first2=Erick J.| last3=Vilhena| first3=Daril A.| last4=Freudenberg| first4=James S.| last5=Bergstrom| first5=Carl T.| bibcode=2012PLoSO...738398C| arxiv=1106.2573| doi-access=free}}</ref>
His work on network control and [[observability]] brought the tools of [[control theory]] to network science. It asked how to identify the nodes from which one can control a complex network, just like a car is controlled through three control points, the steering wheel, gas pedal and the brake. He developed the analytical formalism of controlling complex networks, by mapping the control problem, widely studied in physics and engineering since [[James Clerk Maxwell|Maxwell]], into [[graph matching]], a well-studied graph theoretic problem, merging statistical mechanics and control theory<ref>{{Cite journal |last=Liu, Y. Y., Slotine, J. J., & Barabási, A. L. |date=2011 |title=Controllability of complex networks |journal=Nature |volume=473 |issue=7346 |pages=167-173}}</ref>. By establishing an exact mapping between the dynamical control problem and [[matching (graph theory)|matching]] theory, he developed 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 not only to the discovery of new neurons involved in the control of locomotion, but also offering falsifiable experimental confirmation of control principles<ref>{{Cite journal |last=Yan, G., Vértes, P. E., Towlson, E. K., Chew, Y. L., Walker, D. S., Schafer, W. R., & Barabási, A. L. |date=2017 |title=Network control principles predict neuron function in the Caenorhabditis elegans connectome. |journal=Nature |volume=550 |issue=7677 |pages=519-523}}</ref>.


==Awards==
==Awards==

Revision as of 14:22, 24 October 2022

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
CitizenshipRomanian
Hungarian
American
Alma materUniversity of Bucharest (BS)
Eötvös Loránd University (MS)
Boston University (PhD)
Known forResearch of network theory
the concept of scale-free networks
Proposal of Barabási–Albert model
AwardsFellow of the Network Science Society (NetSci), 2021.
Scientific career
FieldsPhysics
ThesisGrowth and roughening of non-equilibrium interfaces (1994)
Doctoral advisorH. Eugene Stanley
Doctoral studentsGinestra Bianconi
Websitebarabasi.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 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 grew out of and sponsors the flagship NetSci conference held yearly since 2006.

Birth and education

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

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 and the statistical physics of complex systems.

Scale-Free Networks

His biggest role has been the discovery of the scale-free networks. He reported the scale-free nature of the WWW in 1999 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."[10]

He subsequently showed that the scale-free property emerges in biological systems, namely in metabolic networks and protein–protein interaction networks. Science celebrated the ten-year anniversary of Barabási’s 1999 discovery by devoting a special issue to Complex Systems and Networks in 2009.[11][12]

Network Robustness

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.[13] Specifically, they showed that networks can easily survive the random failure of a very large number of nodes, i.e. the failure threshold is high. In contrast, the same networks collapse of they are attacked, by removing the biggest hubs first. The threshold characterizing the breakdown of a network under random failures was later analytically derived[14] , linking it to the second moment of the degree distribution. The threshold converges to zero for large networks, indicating that large networks can easily survive the failure of a very large fraction of their nodes. The calculations also showed that robustness to random failures is not limited to scale-free networks, but it 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, coined and popularized in a scientific article called "Network Medicine – From Obesity to the "Diseasome", published in The New England Journal of Medicine, in 2007[15]. His work introduced the concept of diseasome, or disease network[16], showing how diseases link to each other through shared genes, capturing their common genetic roots. He subsequently pioneered the use of large patient data to explore disease comorbidity, linking it to molecular network data.[17] He subsequently discovered that genes associated with the same disease tend to be located in the same network neighborhood[18], introducing the concept of disease module, currently used to aid drug discovery, drug design, and the development of biomarkers for disease detection. His work has led to the founding of the Channing Division of Network Medicine at Harvard Medical School and to the founding of the Network Medicine Institute and Global Alliance, representing 33 leading universities and institutions around the world committed to advancing the field of Network Medicine. Network medicine has led to multiple experimentally falsifiable predictions, successfully tested, providing experimentally validated novel pathways in asthma, predicting novel mechanism of action for rosmarinic acid, and predicting novel therapeutic functions of existing drugs, and testing them directly in patients (drug repurposing). During COVID  Barabási has led a major collaboration involving researchers at Harvard University, Boston University and Broad Institute, predicting and experimentally testing the potential efficacy for COVID patients of 6,000 approved drugs[19].

Human Dynamics

His work on human dynamics resulted in the discovery of the fat tailed nature of the inter event times in human activity patterns, and proposed the Barabási model[20] that showed that a queuing model was capable of explaining the bursty nature of human activity. This topic is covered by his book Bursts.[citation needed]

Network Control

His work on network control and observability brought the tools of control theory to network science. It asked how to identify the nodes from which one can control a complex network, just like a car is controlled through three control points, the steering wheel, gas pedal and the brake. He developed the analytical formalism of controlling complex networks, by mapping the control problem, widely studied in physics and engineering since Maxwell, into graph matching, a well-studied graph theoretic problem, merging statistical mechanics and control theory[21]. By establishing an exact mapping between the dynamical control problem and matching theory, he developed 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 not only to the discovery of new neurons involved in the control of locomotion, but also offering falsifiable experimental confirmation of control principles[22].

Awards

He was elected a Fellow of the American Physical Society in 2003.[23] In 2005, he was awarded the FEBS Anniversary Prize for Systems Biology and 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.[24]

In 2004, he was elected as an external member of the Hungarian Academy of Sciences. In 2007, he was inducted into the Academia Europaea.[25]

In 2008 he received the 2008 C&C Prize, Japan "for stimulating innovative research on networks and discovering that the scale-free property is a common feature of various real-world complex networks"[26] and the Cozzarelli Prize, National Academies of Sciences (USA)[27]

The Lagrange Prize-Crt Foundation was awarded to Barabási in June 2011, and in November 2011 he was awarded Honorary degree Doctor Honoris Causa by Technical University of Madrid.[28] In 2017 he received the Senior scientific award of the Complex Systems Society for "setting the basis of what is now modern Network Science".[29] In 2018 Barabási has received an honorary doctorate from Utrecht University at the occasion of her 382th Dies Natalis.[30]

In June 2018 he was elected member of the Romanian Academy of Sciences.[31]

The Bolyai Prize was awarded to Mr. Barabási in May 2019 by the Hungarian Academy of Sciences, handed over by the President of Hungary, János Áder.

He is ranked 2nd in the world in the field of Engineering and Technology.[32]

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, 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)

References

  1. ^ People at Center for Network Science, Central European University website; accessed January 10, 2016.
  2. ^ "NetSci – the Network Science Society".
  3. ^ a b Dale Keiger, "Looking for the next big thing", Notre Dame Magazine, vol. 36 (Spring 2007), no. 1, 49–53 Archived May 9, 2008, at the Wayback Machine
  4. ^ "H. Eugene Stanley: Ph.D. Theses Supervised". Polymer.bu.edu. Retrieved January 11, 2016.
  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.
  7. ^ "Albert-László Barabási CV" (PDF). Archived from the original (PDF) on March 3, 2016. Retrieved January 10, 2016.
  8. ^ "ETSI de Telecomunicación: ALBERT LASZLÓ BARABÁSI". www.etsit.upm.es. Retrieved November 15, 2020.
  9. ^ "Albert-László Barabási – Khoury College of Computer Sciences". Retrieved November 15, 2020.
  10. ^ Profile Archived March 9, 2005, at the Wayback Machine, sociopranos.com; accessed 10 January 2016.
  11. ^ Barabasi, Albert-Laszlo (2009). "Scale-Free Networks: A Decade and Beyond". Science. 325 (5939): 412–413.
  12. ^ Jasny, Barbara (2009). "Connections". Science. 325 (5939): 405.
  13. ^ Barabási, Albert-László (July 21, 2016). Network science. Cambridge, United Kingdom. ISBN 9781107076266.{{cite book}}: CS1 maint: location missing publisher (link)
  14. ^ Cohen, Reuven; Erez, Keren; ben-Avraham, Daniel; Havlin, Shlomo (2000). ""Resilience of the Internet to Random Breakdowns"". Physical Review Letters. 85 (21): 4626–4628.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  15. ^ Barabási AL. "Network medicine--from obesity to the "diseasome"". N Engl J Med. 357 (4): 404–407.
  16. ^ Goh, K. I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A. L. (2007). "The human disease network". Proceedings of the National Academy of Sciences. 104 (21): 8685–8690.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  17. ^ 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.
  18. ^ Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barabási AL. (2005). "Uncovering disease-disease relationships through the incomplete interactome". Science. 347 (6224): 1257601.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  19. ^ Morselli Gysi, D., Do Valle, Í., Zitnik, M., Ameli, A., Gan, X., Varol, O., Ghiassian, S.D., Patten, J.J., Davey, R.A., Loscalzo, J. and Barabási, A.L. (2021). "Network medicine framework for identifying drug-repurposing opportunities for COVID-19". Proceedings of the National Academy of Sciences. 118 (19): e2025581118.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  20. ^ A.-L. Barabási (2005). "The origin of bursts and heavy tails in human dynamics". Nature. 435 (7039): 207–11. arXiv:cond-mat/0505371. Bibcode:2005Natur.435..207B. doi:10.1038/nature03459. PMID 15889093. S2CID 4419475.
  21. ^ Liu, Y. Y., Slotine, J. J., & Barabási, A. L. (2011). "Controllability of complex networks". Nature. 473 (7346): 167–173.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  22. ^ Yan, G., Vértes, P. E., Towlson, E. K., Chew, Y. L., Walker, D. S., Schafer, W. R., & Barabási, A. L. (2017). "Network control principles predict neuron function in the Caenorhabditis elegans connectome". Nature. 550 (7677): 519–523.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  23. ^ "APS Fellow Archive". APS. Retrieved September 15, 2020.
  24. ^ "Barabasi co edits new book and is awarded computing medal". Nd.edu. Archived from the original on March 3, 2016. Retrieved January 11, 2016.
  25. ^ "Northeastern Physicist Albert-László Barabási Receives Prestigious Honor for Exemplary Contributions to Interdisciplinary Science" Archived August 26, 2016, at the Wayback Machine, Northeastern University website; accessed January 10, 2016.
  26. ^ "NEC C&C Foundation". Candc.or.jp. Retrieved January 11, 2016.
  27. ^ "PNAS announces 2008 Cozzarelli Prize recipients". EurekAlert!. February 23, 2009. Retrieved January 11, 2016.
  28. ^ [1] Archived January 8, 2012, at the Wayback Machine
  29. ^ "CSS Awards". cssociety.org. Retrieved April 17, 2019.
  30. ^ "Honorary doctorate for Prof. Albert-László Barabási". Utrecht University. March 27, 2018. Retrieved April 17, 2019.
  31. ^ "Comunicat de presă - Academia Română – AGERPRES".
  32. ^ "Research.com - Leading Academic Research Portal". Research.com. Retrieved March 30, 2022.

External links

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