What is he best known for?
The fields of study he is best known for:
- Computer network
- The Internet
- Statistics
The scientist’s investigation covers issues in Complex network, Graph theory, Discrete mathematics, Markov chain and Adjacency matrix.
A large part of his Complex network studies is devoted to Small-world network.
His Small-world network research incorporates elements of Modularity, Sociotechnical system, Complex contagion, Emotional contagion and Scale-free network.
Piet Van Mieghem combines subjects such as Topology, Theoretical computer science, Network dynamics and Graph with his study of Graph theory.
He works mostly in the field of Discrete mathematics, limiting it down to concerns involving Degree and, occasionally, Tree, Random graph and Random regular graph.
His Adjacency matrix research integrates issues from Complex system, Upper and lower bounds, Statistical physics and Markov process.
His most cited work include:
- Epidemic processes in complex networks (1897 citations)
- Virus spread in networks (763 citations)
- Graph Spectra for Complex Networks (461 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Discrete mathematics, Combinatorics, Distributed computing, Complex network and Computer network.
His work carried out in the field of Discrete mathematics brings together such families of science as Upper and lower bounds and Shortest path problem.
His Distributed computing research is multidisciplinary, incorporating elements of Routing table, Network topology, Static routing, Node and Robustness.
His research in Static routing tackles topics such as Destination-Sequenced Distance Vector routing which are related to areas like Quality of service and Equal-cost multi-path routing.
His research integrates issues of Algebraic connectivity, Theoretical computer science and Topology in his study of Complex network.
He has researched Adjacency matrix in several fields, including Statistical physics and Markov process.
He most often published in these fields:
- Discrete mathematics (17.83%)
- Combinatorics (16.09%)
- Distributed computing (14.78%)
What were the highlights of his more recent work (between 2018-2021)?
- Epidemic model (6.09%)
- Topology (10.43%)
- Complex network (12.61%)
In recent papers he was focusing on the following fields of study:
Piet Van Mieghem mainly investigates Epidemic model, Topology, Complex network, Robustness and Adjacency matrix.
His work carried out in the field of Topology brings together such families of science as Network topology, Voltage drop, Path graph and Complete graph.
His studies deal with areas such as Data mining, Feature, Controllability, Network controllability and Community structure as well as Complex network.
His Robustness study integrates concerns from other disciplines, such as Network performance, Distributed computing and Connected component.
His Adjacency matrix research is multidisciplinary, incorporating perspectives in Betweenness centrality, Statistical physics, Laplacian matrix and Spectral gap.
His research investigates the link between Topology and topics such as Markov chain that cross with problems in Markov process.
Between 2018 and 2021, his most popular works were:
- The simplex geometry of graphs (15 citations)
- Mapping functional brain networks from the structural connectome : Relating the series expansion and eigenmode approaches (14 citations)
- Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei (13 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- The Internet
- Statistics
Inference, Epidemic model, Applied mathematics, Statistical physics and Adjacency matrix are his primary areas of study.
His Inference research includes elements of Almost surely and Algorithm, Computation, Prediction algorithms.
His Applied mathematics research is multidisciplinary, relying on both State vector and Differential equation.
His Statistical physics study combines topics from a wide range of disciplines, such as Zero and Complex network.
His Adjacency matrix research focuses on Functional magnetic resonance imaging and how it connects with State.
His study in State is interdisciplinary in nature, drawing from both Linear system and Exponential stability, Stability.
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