What is he best known for?
The fields of study he is best known for:
- Combinatorics
- Discrete mathematics
- Mathematical analysis
Combinatorics, Discrete mathematics, Random graph, Random regular graph and Almost surely are his primary areas of study.
His research on Combinatorics frequently connects to adjacent areas such as Upper and lower bounds.
His work in Discrete mathematics addresses subjects such as Degree, which are connected to disciplines such as Algorithm.
While the research belongs to areas of Random graph, he spends his time largely on the problem of Hypergraph, intersecting his research to questions surrounding Space.
His studies deal with areas such as Tree, Sequence, Cavity method and Linear number as well as Random regular graph.
The Almost surely study combines topics in areas such as Modular decomposition, Trapezoid graph, Pathwidth and Indifference graph.
His most cited work include:
- A critical point for random graphs with a given degree sequence (1954 citations)
- The Size of the Giant Component of a Random Graph with a Given Degree Sequence (700 citations)
- Further algorithmic aspects of the local lemma (173 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Combinatorics, Discrete mathematics, Random graph, Graph and Constraint satisfaction problem.
His work in Degree, Chromatic scale, Random regular graph, Vertex and Hypergraph is related to Combinatorics.
Michael Molloy works mostly in the field of Discrete mathematics, limiting it down to concerns involving Almost surely and, occasionally, Algorithm.
His work on Giant component as part of his general Random graph study is frequently connected to High probability, thereby bridging the divide between different branches of science.
His study in Graph is interdisciplinary in nature, drawing from both Upper and lower bounds and Conjecture.
His Constraint satisfaction problem study combines topics in areas such as Constraint satisfaction and Constraint logic programming.
He most often published in these fields:
- Combinatorics (80.00%)
- Discrete mathematics (56.67%)
- Random graph (18.00%)
What were the highlights of his more recent work (between 2011-2020)?
- Combinatorics (80.00%)
- Discrete mathematics (56.67%)
- Random graph (18.00%)
In recent papers he was focusing on the following fields of study:
Michael Molloy mainly focuses on Combinatorics, Discrete mathematics, Random graph, Cluster analysis and Chromatic scale.
His Combinatorics study frequently draws parallels with other fields, such as Upper and lower bounds.
His study in the field of Multigraph also crosses realms of Constraint satisfaction problem.
He has researched Random graph in several fields, including Random regular graph and Linear number.
The various areas that Michael Molloy examines in his Linear number study include Almost surely, Satisfiability, Cavity method and Exponential function.
His work carried out in the field of Chromatic scale brings together such families of science as Time complexity, Complete coloring and Corollary.
Between 2011 and 2020, his most popular works were:
- The freezing threshold for k-colourings of a random graph (58 citations)
- The list chromatic number of graphs with small clique number (35 citations)
- The scaling window for a random graph with a given degree sequence (34 citations)
In his most recent research, the most cited papers focused on:
- Combinatorics
- Mathematical analysis
- Discrete mathematics
His primary areas of investigation include Combinatorics, Discrete mathematics, Sequence, Hypergraph and Random graph.
The study of Combinatorics is intertwined with the study of Constant in a number of ways.
His studies in Discrete mathematics integrate themes in fields like Almost surely and Exponential function.
His biological study spans a wide range of topics, including Binary logarithm and Degree.
His Random graph research integrates issues from Window and Linear number.
His Linear number research incorporates elements of Tree, Random regular graph, Satisfiability and Cavity method.
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