What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Computer network
His scientific interests lie mostly in Complex network, Theoretical computer science, Scale-free network, Social system and Complex system.
His Complex network research incorporates themes from Critical phenomena, Statistical physics, Distributed computing and Topology.
His Theoretical computer science study combines topics from a wide range of disciplines, such as Range, Generalization, Centrality and Emotional contagion.
His Scale-free network research is multidisciplinary, incorporating perspectives in Node, Topology, Interdependent networks and Social network.
His work carried out in the field of Interdependent networks brings together such families of science as Human dynamics, Dynamical systems theory and Network dynamics.
As a member of one scientific family, Yamir Moreno mostly works in the field of Complex system, focusing on Network theory and, on occasion, Construct and Connected component.
His most cited work include:
- Complex networks: Structure and dynamics (7818 citations)
- Synchronization in complex networks (2397 citations)
- Multilayer Networks (1360 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Complex network, Statistical physics, Theoretical computer science, Complex system and Social system.
His Complex network study integrates concerns from other disciplines, such as Evolutionary dynamics, Network topology and Topology.
His biological study spans a wide range of topics, including Microeconomics, Public goods game and Prisoner's dilemma.
His research in Statistical physics intersects with topics in Phase transition, Eigenvalues and eigenvectors and Inverse.
His Theoretical computer science research is multidisciplinary, incorporating elements of Null model, Centrality and Structure.
His research integrates issues of Social media, Replicator equation and Data science in his study of Social system.
He most often published in these fields:
- Complex network (29.32%)
- Statistical physics (21.94%)
- Theoretical computer science (21.10%)
What were the highlights of his more recent work (between 2019-2021)?
- Statistical physics (21.94%)
- Theoretical computer science (21.10%)
- Phase transition (6.12%)
In recent papers he was focusing on the following fields of study:
Yamir Moreno mainly investigates Statistical physics, Theoretical computer science, Phase transition, Complex network and Complex system.
His work deals with themes such as Hypergraph, Structure, Fixed point, Dynamical systems theory and Stability, which intersect with Statistical physics.
His research on Theoretical computer science also deals with topics like
- Null model which connect with Bipartite graph and Complement,
- Replicator equation, which have a strong connection to Social network.
His studies in Phase transition integrate themes in fields like Adjacency matrix, Universality and Inverse.
Yamir Moreno integrates many fields, such as Complex network and Disease spreading, in his works.
His Complex system study also includes
- Relational database and related Link, Evolving networks, Multiplex and Bounded function,
- Triadic closure together with Metric.
Between 2019 and 2021, his most popular works were:
- Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. (183 citations)
- Modeling the impact of social distancing, testing, contact tracing and household quarantine on second-wave scenarios of the COVID-19 epidemic. (88 citations)
- Social contagion models on hypergraphs (41 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Computer network
Yamir Moreno spends much of his time researching Statistical physics, Psychological intervention, Pandemic, Data-driven and Development economics.
His Statistical physics research includes themes of Phase space, Social behavior, Hysteresis, Bistability and Stability conditions.
His study in Data-driven is interdisciplinary in nature, drawing from both Mixing and Data mining.
Along with Development economics, other disciplines of study including History, Context, Optimism bias and Risk perception are integrated into his research.
He combines subjects such as Public economics, Health care, Quarantine and Identification with his study of Herd immunity.
His Transmission study incorporates themes from Secondary infection and Environmental health.
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.
