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Steven Henry Strogatz (/ ˈ s t r oʊ ɡ æ t s /; born August 13, 1959) is an American mathematician and author, and the Susan and Barton Winokur Distinguished Professor for the Public Understanding of Science and Mathematics at Cornell University.
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The Watts–Strogatz model is a random graph generation model that produces graphs with small-world properties, including short average path lengths and high clustering. It was proposed by Duncan J. Watts and Steven Strogatz in their article published in 1998 in the Nature scientific journal. [ 1 ]
Watts and Strogatz then proposed a novel graph model, currently named the Watts and Strogatz model, with (i) a small average shortest path length, and (ii) a large clustering coefficient. The crossover in the Watts–Strogatz model between a "large world" (such as a lattice) and a small world was first described by Barthelemy and Amaral in 1999 ...
Steven Strogatz (born 1959), nonlinear systems and applied mathematics [6] Daniel Stroock (born 1940), probability theory [6] Eduard Study (1862–1930), invariant theory and geometry [118]: 88 Bella Subbotovskaya (1938–1982), mathematician and founder of the Jewish People's University [482] Benny Sudakov (born 1969), combinatorics [9]
In the context of network theory, a complex network is a graph (network) with non-trivial topological features—features that do not occur in simple networks such as lattices or random graphs but often occur in networks representing real systems.
In graph theory, a clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. Evidence suggests that in most real-world networks, and in particular social networks, nodes tend to create tightly knit groups characterised by a relatively high density of ties; this likelihood tends to be greater than the average probability of a tie randomly established ...
The existence of ripple solutions was predicted (but not observed) by Wiley, Strogatz and Girvan, [20] who called them multi-twisted q-states. The topology on which the Kuramoto model is studied can be made adaptive [ 21 ] by use of fitness model showing enhancement of synchronization and percolation in a self-organised way.