What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Statistics
- Geometry
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 most cited work include:
- Structure and tie strengths in mobile communication networks (1505 citations)
- Detecting the overlapping and hierarchical community structure in complex networks (1401 citations)
- Detecting the overlapping and hierarchical community structure of complex networks (972 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Statistical physics (36.70%)
- Econometrics (26.02%)
- Scaling (16.70%)
What were the highlights of his more recent work (between 2015-2021)?
- Social network (20.97%)
- Statistical physics (36.70%)
- Homophily (5.63%)
In recent papers he was focusing on the following fields of study:
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.
Between 2015 and 2021, his most popular works were:
- Communication with family and friends across the life course (41 citations)
- Communication with family and friends across the life course (41 citations)
- Communication with family and friends across the life course (41 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Statistics
- Geometry
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.
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