What is he best known for?
The fields of study he is best known for:
- Combinatorics
- Discrete mathematics
- Algorithm
Alan Frieze mainly focuses on Combinatorics, Discrete mathematics, Random graph, Random regular graph and Random walk.
Alan Frieze interconnects Algorithm and Simple in the investigation of issues within Combinatorics.
His work in Discrete mathematics is not limited to one particular discipline; it also encompasses Matrix.
His studies in Random graph integrate themes in fields like Indifference graph, Hopcroft–Karp algorithm, Graph product, Constant and struct.
His Random regular graph research is multidisciplinary, incorporating perspectives in Expected value, Bound graph and Degree.
His study on Random walk also encompasses disciplines like
- Isoperimetric inequality which intersects with area such as Correctness,
- Regular graph which intersects with area such as Stationary distribution.
His most cited work include:
- Min-Wise Independent Permutations (753 citations)
- A random polynomial-time algorithm for approximating the volume of convex bodies (591 citations)
- Quick Approximation to Matrices and Applications (443 citations)
What are the main themes of his work throughout his whole career to date?
Alan Frieze mainly investigates Combinatorics, Discrete mathematics, Random graph, Graph and Random regular graph.
Vertex, Hamiltonian path, Binary logarithm, Vertex and Hypergraph are the core of his Combinatorics study.
His study connects Random walk and Discrete mathematics.
In his research, Matching is intimately related to Bipartite graph, which falls under the overarching field of Random graph.
The various areas that Alan Frieze examines in his Random regular graph study include Pancyclic graph and Graph power, Factor-critical graph, Regular graph.
The subject of his Time complexity research is within the realm of Algorithm.
He most often published in these fields:
- Combinatorics (84.47%)
- Discrete mathematics (51.94%)
- Random graph (36.25%)
What were the highlights of his more recent work (between 2016-2021)?
- Combinatorics (84.47%)
- Random graph (36.25%)
- Graph (16.83%)
In recent papers he was focusing on the following fields of study:
Combinatorics, Random graph, Graph, Vertex and Random walk are his primary areas of study.
His studies deal with areas such as Discrete mathematics and Constant as well as Combinatorics.
In general Random graph study, his work on Giant component often relates to the realm of High probability, thereby connecting several areas of interest.
His research in the fields of Bipartite graph overlaps with other disciplines such as Sigma and Omega.
His Vertex study combines topics in areas such as Upper and lower bounds and Cubic graph.
His work focuses on many connections between Random walk and other disciplines, such as Hash function, that overlap with his field of interest in Bin.
Between 2016 and 2021, his most popular works were:
- Assessing significance in a Markov chain without mixing. (45 citations)
- Hamilton Cycles in Random Graphs: a bibliography (15 citations)
- Embedding the Erdős–Rényi hypergraph into the random regular hypergraph and Hamiltonicity (13 citations)
In his most recent research, the most cited papers focused on:
- Combinatorics
- Algorithm
- Statistics
His main research concerns Combinatorics, Random graph, Hamiltonian path, Graph and Vertex.
His work carried out in the field of Combinatorics brings together such families of science as Discrete mathematics and Constant.
His research integrates issues of Voter model, Bounded function and Asynchronous communication in his study of Discrete mathematics.
Alan Frieze focuses mostly in the field of Random graph, narrowing it down to matters related to Longest path problem and, in some cases, Polynomial and Polynomial.
His Hamiltonian path study incorporates themes from Hypergraph, Embedding, Set and Random regular graph.
His work on Giant component as part of general Graph study is frequently connected to Omega, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
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.
