What is he best known for?
The fields of study he is best known for:
- Statistics
- Quantum mechanics
- Artificial intelligence
His primary scientific interests are in Modularity, Mobile phone, Distributed computing, Graph and Theoretical computer science.
His Modularity study combines topics from a wide range of disciplines, such as Context, Community structure and Artificial intelligence.
Clique percolation method is the focus of his Community structure research.
The concepts of his Clique percolation method study are interwoven with issues in Computation and Girvan–Newman algorithm.
His work carried out in the field of Distributed computing brings together such families of science as Physical Internet, Space and Network topology.
The study incorporates disciplines such as Complex network, Information processing and Random graph in addition to Graph.
His most cited work include:
- Fast unfolding of communities in large networks (9882 citations)
- Fast unfolding of communities in large networks (9882 citations)
- Modular and Hierarchically Modular Organization of Brain Networks (747 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Statistical physics, Complex network, Theoretical computer science, Random walk and Artificial intelligence.
His Statistical physics research is multidisciplinary, incorporating perspectives in Structure, Stochastic process and Node.
His research in the fields of Network science and Degree distribution overlaps with other disciplines such as Burstiness.
Renaud Lambiotte has researched Theoretical computer science in several fields, including Consensus dynamics, Modularity, Graph and Complex system.
His Modularity research incorporates elements of Community structure and Mobile phone.
His research investigates the link between Artificial intelligence and topics such as Machine learning that cross with problems in Social network.
He most often published in these fields:
- Statistical physics (27.38%)
- Complex network (19.44%)
- Theoretical computer science (19.05%)
What were the highlights of his more recent work (between 2017-2021)?
- Theoretical computer science (19.05%)
- Statistical physics (27.38%)
- Random walk (14.68%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Theoretical computer science, Statistical physics, Random walk, Nonlinear system and Complex network.
His Theoretical computer science research includes elements of Probability and statistics, Complex system, Network science and Topological data analysis.
His biological study spans a wide range of topics, including Modularity, Modularity maximization and Community structure.
In Community structure, Renaud Lambiotte works on issues like Benchmark, which are connected to Data science.
His study in Consensus dynamics is interdisciplinary in nature, drawing from both Group dynamic, Graph and Pairwise comparison.
In his study, Social network analysis is strongly linked to Invariant, which falls under the umbrella field of Graph.
Between 2017 and 2021, his most popular works were:
- Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. (172 citations)
- Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. (172 citations)
- From networks to optimal higher-order models of complex systems (97 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Quantum mechanics
- Artificial intelligence
His main research concerns Theoretical computer science, Network science, Statistical physics, Complex network and Pairwise comparison.
Renaud Lambiotte has included themes like Probability and statistics, Complex system, Modularity and Topological data analysis in his Theoretical computer science study.
His research in Modularity intersects with topics in Machine learning and Assortative mixing.
His Network science study combines topics in areas such as Data science and Community structure, Modularity maximization.
His studies in Community structure integrate themes in fields like Stochastic block model and Benchmark.
His Complex network study integrates concerns from other disciplines, such as Online discussion, Graph and The Internet.
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