2022 - Research.com Engineering and Technology in Hungary Leader Award
János Kertész mainly focuses on Complex network, Statistical physics, Econometrics, Complex system and Minimum spanning tree. János Kertész has included themes like Discrete mathematics, Theoretical computer science and Clustering coefficient in his Complex network study. János Kertész has researched Theoretical computer science in several fields, including Clique percolation method and Community structure.
His Clustering coefficient research integrates issues from Statistical mechanics and Combinatorics. His work deals with themes such as Distribution, Nonlinear system, Exponent and Autocorrelation, which intersect with Statistical physics. His research in Econometrics intersects with topics in Stock return, Covariance matrix, Correlation and Stock market.
His scientific interests lie mostly in Statistical physics, Econometrics, Scaling, Social network and Complex network. His work focuses on many connections between Statistical physics and other disciplines, such as Exponent, that overlap with his field of interest in Power law. His research integrates issues of Hurst exponent, Stock market and Epps effect in his study of Econometrics.
In most of his Scaling studies, his work intersects topics such as Condensed matter physics. His Social network research is multidisciplinary, incorporating elements of Microeconomics, Theoretical computer science and Data mining. His work carried out in the field of Complex network brings together such families of science as Algorithm and Community structure.
János Kertész spends much of his time researching Social network, Statistical physics, Homophily, Microeconomics and Phase transition. His biological study spans a wide range of topics, including Theoretical computer science, Service, Product, Social influence and Data science. His studies deal with areas such as Digital media, Phenomenon, Communication channel, Popularity and Big data as well as Theoretical computer science.
In Statistical physics, János Kertész works on issues like Interdependent networks, which are connected to Scaling and Universality. His Homophily research includes elements of Function, Feature, Clustering coefficient and Complex network. His Complex network research is multidisciplinary, incorporating elements of Transitive relation, Community structure and Kinship.
János Kertész mainly investigates Social network, Statistical physics, Phase transition, Theoretical computer science and Function. János Kertész combines subjects such as Range, Structure, Econometrics and Benchmark with his study of Social network. His work investigates the relationship between Statistical physics and topics such as Scaling that intersect with problems in Universality.
His Phase transition study combines topics in areas such as Simplex, Power law and Complex network. His studies in Theoretical computer science integrate themes in fields like Sampling, Assortativity, Sample, Degree distribution and Big data. His Function study incorporates themes from Feature, Clustering coefficient, Homophily and Community structure.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Structure and tie strengths in mobile communication networks
J.-P. Onnela;J. Saramäki;J. Hyvönen;G. Szabó.
Proceedings of the National Academy of Sciences of the United States of America (2007)
Detecting the overlapping and hierarchical community structure in complex networks
Andrea Lancichinetti;Santo Fortunato;János Kertész.
New Journal of Physics (2009)
Detecting the overlapping and hierarchical community structure of complex networks
Andrea Lancichinetti;Santo Fortunato;Janos Kertesz.
arXiv: Physics and Society (2008)
Intensity and coherence of motifs in weighted complex networks
Jukka Pekka Onnela;Jari Saramäki;János Kertész;János Kertész;Kimmo Kaski.
Physical Review E (2005)
Dynamics of market correlations: taxonomy and portfolio analysis.
Jukka-Pekka Onnela;Anirban Chakraborti;Kimmo Kaski;Janos Kertesz;Janos Kertesz.
Physical Review E (2003)
Small but slow world: how network topology and burstiness slow down spreading
Márton Karsai;Mikko Kivelä;Raj Kumar Pan;Kimmo Kaski.
Physical Review E (2011)
Generalizations of the clustering coefficient to weighted complex networks
Jari Saramäki;Mikko Kivelä;Jukka-Pekka Onnela;Jukka-Pekka Onnela;Kimmo Kaski.
Physical Review E (2007)
Analysis of a large-scale weighted network of one-to-one human communication
Jukka-Pekka Onnela;Jukka-Pekka Onnela;Jari Saramäki;Jörkki Hyvönen;Gábor Szabó;Gábor Szabó.
New Journal of Physics (2007)
Manifesto of computational social science
R. Conte;N. Gilbert;G. Bonelli;C. Cioffi-Revilla.
Clustering and information in correlation based financial networks
Jukka-Pekka Onnela;Kimmo Kaski;Janos Kertész;Janos Kertész.
European Physical Journal B (2004)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: