Roberta Sinatra

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Roberta Sinatra
Alma materUniversity of Catania
Scientific career
Thesis High-order markov chains in complex networks: models and applications  (2012)

Roberta Sinatra [1] is an Italian scientist and associate professor at the IT University of Copenhagen. She is known for her work in network science and conducts research on quantifying success in science.

Contents

Early life and education

Sinatra received her undergraduate degree from the University of Catania in 2005. She received her M.Sc. in theoretical physics from the same university in 2007 and, in 2008, an M.Sc. (Eccellenza Specialistica) from the Scuola superiore di Catania. [2] She went on to earn a Ph.D in physics from the University of Catania in 2012, working with Vito Latora and Jesús Gómez-Gardeñes. [3] She then held postdoctoral and associate research scientist positions at Northeastern University and Dana Farber Cancer Institute. She joined IT University of Copenhagen in 2019. [2]

Research and career

Sinatra is known for her research on the social determinants of success, using large-scale data sets and methods from statistical physics, machine learning and network science. [4] Her early work was on cooperation in games. [5] She has subsequently quantified performance and success in scientific and creative careers, [6] [7] [8] as well as in art and culture. [9] [10] Through her research, she addressed gender inequality in academic publishing [11] [12] and the importance of luck in success. [8] [13]

Selected publications

Awards and honors

In 2017 she was named a fellow of the Institute for Scientific Interchange. [14] [15] In 2020 Sinatra received a Junior Scientific Award from the Complex System Society, for "pioneer contributions to the science of science and success, having had an impact in multiple fields, from network science to computational social science and scientometrics". [16] [17]

Related Research Articles

<span class="mw-page-title-main">Scale-free network</span> Network whose degree distribution follows a power law

A scale-free network is a network whose degree distribution follows a power law, at least asymptotically. That is, the fraction P(k) of nodes in the network having k connections to other nodes goes for large values of k as

<span class="mw-page-title-main">Network theory</span> Study of graphs as a representation of relations between discrete objects

In mathematics, computer science and network science, network theory is a part of graph theory. It defines networks as graphs where the nodes or edges possess attributes. Network theory analyses these networks over the symmetric relations or asymmetric relations between their (discrete) components.

<span class="mw-page-title-main">Albert-László Barabási</span> Hungarian-American physicist (born 1967)

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

<span class="mw-page-title-main">Complex network</span> Network with non-trivial topological features

In the context of network theory, a complex network is a graph (network) with non-trivial topological features—features that do not occur in simple networks such as lattices or random graphs but often occur in networks representing real systems. The study of complex networks is a young and active area of scientific research inspired largely by empirical findings of real-world networks such as computer networks, biological networks, technological networks, brain networks, climate networks and social networks.

Landauer's principle is a physical principle pertaining to the lower theoretical limit of energy consumption of computation. It holds that an irreversible change in information stored in a computer, such as merging two computational paths, dissipates a minimum amount of heat to its surroundings.

<span class="mw-page-title-main">Barabási–Albert model</span> Scale-free network generation algorithm

The Barabási–Albert (BA) model is an algorithm for generating random scale-free networks using a preferential attachment mechanism. Several natural and human-made systems, including the Internet, the World Wide Web, citation networks, and some social networks are thought to be approximately scale-free and certainly contain few nodes with unusually high degree as compared to the other nodes of the network. The BA model tries to explain the existence of such nodes in real networks. The algorithm is named for its inventors Albert-László Barabási and Réka Albert.

Human dynamics refer to a branch of complex systems research in statistical physics such as the movement of crowds and queues and other systems of complex human interactions including statistical modelling of human networks, including interactions over communications networks.

Matthias Staudacher is a German theoretical physicist who has done significant work in the area of quantum field theory and string theory.

In social network analysis, the co-stardom network represents the collaboration graph of film actors i.e. movie stars. The co-stardom network can be represented by an undirected graph of nodes and links. Nodes correspond to the movie star actors and two nodes are linked if they co-starred (performed) in the same movie. The links are un-directed, and can be weighted or not depending on the goals of study. If the number of times two actors appeared in a movie is needed, links are assigned weights. The co-stardom network can also be represented by a bipartite graph where nodes are of two types: actors and movies. And edges connect different types of nodes if they have a relationship. Initially the network was found to have a small-world property. Afterwards, it was discovered that it exhibits a scale-free (power-law) behavior.

Réka Albert is a Romanian-Hungarian scientist. She is a distinguished professor of physics and adjunct professor of biology at Pennsylvania State University and is noted for the Barabási–Albert model and research into scale-free networks and Boolean modeling of biological systems.

Multi-messenger astronomy is astronomy based on the coordinated observation and interpretation of signals carried by disparate "messengers": electromagnetic radiation, gravitational waves, neutrinos, and cosmic rays. They are created by different astrophysical processes, and thus reveal different information about their sources.

<span class="mw-page-title-main">Scientific collaboration network</span>

Scientific collaboration network is a social network where nodes are scientists and links are co-authorships as the latter is one of the most well documented forms of scientific collaboration. It is an undirected, scale-free network where the degree distribution follows a power law with an exponential cutoff – most authors are sparsely connected while a few authors are intensively connected. The network has an assortative nature – hubs tend to link to other hubs and low-degree nodes tend to link to low-degree nodes. Assortativity is not structural, meaning that it is not a consequence of the degree distribution, but it is generated by some process that governs the network’s evolution.

<span class="mw-page-title-main">Individual mobility</span> How humans move within a network

Individual human mobility is the study that describes how individual humans move within a network or system. The concept has been studied in a number of fields originating in the study of demographics. Understanding human mobility has many applications in diverse areas, including spread of diseases, mobile viruses, city planning, traffic engineering, financial market forecasting, and nowcasting of economic well-being.

<span class="mw-page-title-main">Bianconi–Barabási model</span>

The Bianconi–Barabási model is a model in network science that explains the growth of complex evolving networks. This model can explain that nodes with different characteristics acquire links at different rates. It predicts that a node's growth depends on its fitness and can calculate the degree distribution. The Bianconi–Barabási model is named after its inventors Ginestra Bianconi and Albert-László Barabási. This model is a variant of the Barabási–Albert model. The model can be mapped to a Bose gas and this mapping can predict a topological phase transition between a "rich-get-richer" phase and a "winner-takes-all" phase.

In a scale-free network the degree distribution follows a power law function. In some empirical examples this power-law fits the degree distribution well only in the high degree region, however for small degree nodes the empirical degree-distribution deviates from it. See for example the network of scientific citations. This deviation of the observed degree-distribution from the theoretical prediction at the low-degree region is often referred as low-degree saturation.

The initial attractiveness is a possible extension of the Barabási–Albert model. The Barabási–Albert model generates scale-free networks where the degree distribution can be described by a pure power law. However, the degree distribution of most real life networks cannot be described by a power law solely. The most common discrepancies regarding the degree distribution found in real networks are the high degree cut-off and the low degree cut-off. The inclusion of initial attractiveness in the Barabási–Albert model addresses the low-degree cut-off phenomenon.

<span class="mw-page-title-main">Mediation-driven attachment model</span>

In the scale-free network theory, a mediation-driven attachment (MDA) model appears to embody a preferential attachment rule tacitly rather than explicitly. According to MDA rule, a new node first picks a node from the existing network at random and connect itself not with that but with one of the neighbors also picked at random.

<span class="mw-page-title-main">Citation dynamics</span> Number of references received by a scientific work over time

Citation dynamics describes the number of references received by the article or other scientific work over time. The citation dynamics is usually described by the bang, that take place two–three years after the work has been published, and the burst size spans several orders of magnitude. The presence of bursts is not consistent with other models based on preferential attachment. Those models are able to account for the skewed citation distribution but their reference accumulation is gradual.

Ginestra Bianconi is a network scientist and mathematical physicist, known for her work on statistical mechanics, network theory, multilayer and higher-order networks, and in particular for the Bianconi–Barabási model of growing of complex networks and for the Bose–Einstein condensation in complex networks. She is a professor of applied mathematics at Queen Mary University of London, and the editor-in-chief of Journal of Physics: Complexity.

Dashun Wang is a Professor of Management and Organizations at the Kellogg School of Management and the McCormick School of Engineering, at Northwestern University since 2016. At Kellogg from 2019, he is the Founding Director of the Center for Science of Science and Innovation (CSSI). He is also a core faculty at the Northwestern Institute on Complex Systems (NICO) and an Adjunct Professor of Department of Physics, at Northeastern University. His current research focus is on Science of Science. Dashun is a recipient of the AFOSR Young Investigator award (2016) and Poets & Quants Best 40 Under 40 Professors (2019).

References

  1. ↑ "Roberta Sinatra – Associate Professor @ ITU Copenhagen" . Retrieved November 5, 2021.
  2. 1 2 "Bio – Roberta Sinatra" . Retrieved June 7, 2022.
  3. ↑ "High-order markov chains in complex networks: models and applications". 123dok.org (in Italian). Retrieved November 5, 2021.
  4. ↑ Fortunato, Santo; Bergstrom, Carl T.; Börner, Katy; Evans, James A.; Helbing, Dirk; Milojević, Staša; Petersen, Alexander M.; Radicchi, Filippo; Sinatra, Roberta; Uzzi, Brian; Vespignani, Alessandro (March 2, 2018). "Science of science". Science. 359 (6379): eaao0185. doi:10.1126/science.aao0185. PMC   5949209 . PMID   29496846.
  5. ↑ Sinatra, R; Iranzo, J; Gómez-Gardeñes, J; Floría, L M; Latora, V; Moreno, Y (September 21, 2009). "The Ultimatum Game in complex networks". Journal of Statistical Mechanics: Theory and Experiment. 2009 (9): P09012. arXiv: 0807.0750 . Bibcode:2009JSMTE..09..012S. doi:10.1088/1742-5468/2009/09/p09012. ISSN   1742-5468. S2CID   15069048.
  6. ↑ Sinatra, Roberta; Wang, Dashun; Deville, Pierre; Song, Chaoming; Barabási, Albert-László (November 4, 2016). "Quantifying the evolution of individual scientific impact". Science. 354 (6312): aaf5239. doi: 10.1126/science.aaf5239 . PMID   27811240. S2CID   24015860.
  7. ↑ Deville, Pierre; Wang, Dashun; Sinatra, Roberta; Song, Chaoming; Blondel, Vincent D.; Barabási, Albert-László (April 24, 2014). "Career on the Move: Geography, Stratification and Scientific Impact". Scientific Reports. 4 (1): 4770. arXiv: 1404.6247 . Bibcode:2014NatSR...4E4770D. doi:10.1038/srep04770. ISSN   2045-2322. PMC   3998072 . PMID   24759743.
  8. 1 2 Janosov, Milán; Battiston, Federico; Sinatra, Roberta (December 2020). "Success and luck in creative careers". EPJ Data Science. 9 (1): 1–12. arXiv: 1909.07956 . doi: 10.1140/epjds/s13688-020-00227-w . ISSN   2193-1127. S2CID   202583344.
  9. ↑ Fraiberger, Samuel P.; Sinatra, Roberta; Resch, Magnus; Riedl, Christoph; Barabási, Albert-László (November 16, 2018). "Quantifying reputation and success in art". Science. 362 (6416): 825–829. Bibcode:2018Sci...362..825F. doi: 10.1126/science.aau7224 . PMID   30409804. S2CID   53242153.
  10. ↑ Liu, Lu; Wang, Yang; Sinatra, Roberta; Giles, C. Lee; Song, Chaoming; Wang, Dashun (July 2018). "Hot streaks in artistic, cultural, and scientific careers". Nature. 559 (7714): 396–399. arXiv: 1712.01804 . Bibcode:2018Natur.559..396L. doi:10.1038/s41586-018-0315-8. ISSN   1476-4687. PMID   29995850. S2CID   49667579.
  11. ↑ Huang, Junming; Gates, Alexander J.; Sinatra, Roberta; Barabási, Albert-László (March 3, 2020). "Historical comparison of gender inequality in scientific careers across countries and disciplines". Proceedings of the National Academy of Sciences. 117 (9): 4609–4616. arXiv: 1907.04103 . Bibcode:2020PNAS..117.4609H. doi: 10.1073/pnas.1914221117 . ISSN   0027-8424. PMC   7060730 . PMID   32071248.
  12. ↑ Elfman, Lois (February 24, 2020). "Study: Gender Inequality Persists in Science Careers and Publishing". Diverse: Issues In Higher Education. Retrieved June 7, 2022.
  13. ↑ Lu, Donna (September 26, 2019). "Around half of your chances of career success comes down to sheer luck". New Scientist. Retrieved June 7, 2022.
  14. ↑ "Sinatra Awarded ISI Fellowship | Department of Network and Data Science". networkdatascience.ceu.edu. Retrieved November 5, 2021.
  15. ↑ "Roberta Sinatra". isi.it. Retrieved June 7, 2022.
  16. ↑ "CSS Awards". cssociety.org. Retrieved November 5, 2021.
  17. ↑ misz. "Roberta Sinatra receives CSS award | NEtwoRks, Data, and Society (NERDS)" . Retrieved November 5, 2021.
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