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- Nicholas C. Wormald

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Computer Science
D-index
50
Citations
8,607
218
World Ranking
2913
National Ranking
76

Mathematics
D-index
51
Citations
8,617
224
World Ranking
533
National Ranking
16

- Combinatorics
- Geometry
- Algebra

His main research concerns Combinatorics, Discrete mathematics, Random graph, Random regular graph and Chordal graph. His is doing research in Indifference graph, Degree, Giant component, Enumeration and Regular graph, both of which are found in Combinatorics. His research integrates issues of Asymptotic formula and Random element in his study of Discrete mathematics.

His studies in Random graph integrate themes in fields like Hopcroft–Karp algorithm, Random geometric graph and Strength of a graph. Triangle-free graph is closely connected to Split graph in his research, which is encompassed under the umbrella topic of Random regular graph. As a part of the same scientific family, Nicholas C. Wormald mostly works in the field of Chordal graph, focusing on Strongly regular graph and, on occasion, Bipartite graph.

- Sudden Emergence of a Giantk-Core in a Random Graph (375 citations)
- Differential Equations for Random Processes and Random Graphs (354 citations)
- Edge crossings in drawings of bipartite graphs (245 citations)

Nicholas C. Wormald mainly investigates Combinatorics, Discrete mathematics, Random graph, Random regular graph and Graph. Indifference graph, Almost surely, Chordal graph, Degree and Vertex are among the areas of Combinatorics where the researcher is concentrating his efforts. The study incorporates disciplines such as Pathwidth and Split graph in addition to Indifference graph.

His work in the fields of Discrete mathematics, such as Cubic graph, Line graph, Regular graph and Maximal independent set, overlaps with other areas such as Upper and lower bounds. His Random graph study combines topics in areas such as Binary logarithm, Independent set, Greedy algorithm and Random geometric graph. His Random regular graph research is multidisciplinary, incorporating perspectives in Strongly regular graph, Odd graph and Random element.

- Combinatorics (89.40%)
- Discrete mathematics (58.94%)
- Random graph (34.77%)

- Combinatorics (89.40%)
- Random graph (34.77%)
- Discrete mathematics (58.94%)

The scientist’s investigation covers issues in Combinatorics, Random graph, Discrete mathematics, Graph and Vertex. Nicholas C. Wormald regularly links together related areas like Logarithm in his Combinatorics studies. His Random graph research focuses on Binary logarithm and how it relates to Connected component.

His primary area of study in Discrete mathematics is in the field of Random regular graph. His Random regular graph research is multidisciplinary, incorporating elements of Indifference graph and Expander graph. His Vertex research incorporates themes from Distributed algorithm and Orientability.

- The mixing time of the giant component of a random graph (29 citations)
- Enumeration of graphs with a heavy-tailed degree sequence (20 citations)
- Asymptotic enumeration of graphs by degree sequence, and the degree sequence of a random graph (20 citations)

- Combinatorics
- Geometry
- Algebra

His main research concerns Combinatorics, Discrete mathematics, Random graph, Random regular graph and Graph. Almost surely, Vertex, Conjecture, Path and Degree are the subjects of his Combinatorics studies. Nicholas C. Wormald integrates Discrete mathematics with Running time in his study.

His Random graph research integrates issues from Algorithm, Chordal graph and Girth. His Random regular graph research includes elements of Indifference graph, Maximal independent set, Expander graph and Vertex. His Indifference graph study combines topics from a wide range of disciplines, such as Odd graph, Pathwidth, Split graph, Frequency partition of a graph and Asymptotic formula.

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.

Sudden Emergence of a Giantk-Core in a Random Graph

Boris Pittel;Joel Spencer;Nicholas Wormald.

Journal of Combinatorial Theory, Series B **(1996)**

457 Citations

Differential Equations for Random Processes and Random Graphs

Nicholas Charles Wormald.

Annals of Applied Probability **(1995)**

434 Citations

Edge crossings in drawings of bipartite graphs

Peter Eades;Nicholas C. Wormald.

Algorithmica **(1994)**

417 Citations

The differential equation method for random graph processes and greedy algorithms

Nicholas Charles Wormald.

**(1999)**

293 Citations

Generating Random Regular Graphs Quickly

A. Steger;N. C. Wormald.

Combinatorics, Probability & Computing **(1999)**

265 Citations

Almost all regular graphs are hamiltonian

R. W. Robinson;N. C. Wormald.

Random Structures and Algorithms **(1994)**

247 Citations

Asymptotic enumeration by degree sequence of graphs with degrees o ( n 1/2 )

Brendan D. McKay;Nicholas C. Wormald.

Combinatorica **(1991)**

230 Citations

Uniform generation of random regular graphs of moderate degree

Brendan D. McKay;Nicholas C. Wormald.

Journal of Algorithms **(1990)**

192 Citations

Almost all cubic graphs are Hamiltonian

R. W. Robinson;N. C. Wormald.

Random Structures and Algorithms **(1992)**

185 Citations

Some problems in the enumeration of labelled graphs

Nicholas C. Wormald.

Bulletin of The Australian Mathematical Society **(1980)**

168 Citations

Australian National University

The Ohio State University

University of Sydney

Adam Mickiewicz University in Poznań

Tel Aviv University

Tel Aviv University

ETH Zurich

Weizmann Institute of Science

New Mexico State University

Carnegie Mellon University

Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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