Models of the Small World

By Mark Newman. 2000

739 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Mathematical Physics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/92aecb131988306bd089edd3950969bfcf0299dc

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In Atomic and Molecular Physics, and Optics

In Mathematical Physics

In Statistical and Nonlinear Physics

Citations in the corpus

Random Graph Dynamics: References (2006, methodology): “Inspired by Barabási and Albert (1999), Callaway, Hopcroft, Kleinberg, Newman, and Strogatz (2001) introduced the following simple version of a randomly grown graph.”
The Emperor’s Dilemma: A Computational Model of Self‐Enforcing Norms1 (2005, background): “A growing body of theoretical research demonstrates that increasing the proportion of random ties in a regular network dramatically increases the propagation rate of cascades (Newman 2000; Kleinberg 2002).”
Trust Networks on the Semantic Web (2003, background): “Since then, studies have shown that many complex networks share the common features of the small-world phenomenon: small average distance between nodes, and a high connectance, or clustering coefficient [21].”
Random Graphs and Complex Networks (2016, neutral): “Further results are given in [198].”
The Spread of Behavior in an Online Social Network Experiment (2010, neutral): “org/cgi/content/full/329/5996/1194 networks with high levels of local clustering and tightly knit neighborhoods are inefficient for large-scale diffusion processes (4, 5, 9).”
From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks (2013, background): “In particular, the mean geodesic distance in a WS network is ‘small’ relative to the network size - at most a logarithmic function of the total number of nodes in the network 3 - and we now know that several real networks follow this paradigm.”
Supply-chain networks: a complex adaptive systems perspective (2005, background): “In order to describe the transition from a regular network to a random network, Watts and Strogatz introduced the so-called small-world graphs as models of social networks (Watts and Strogatz 1998) and (Newman 2000).”
Selection, Tinkering, and Emergence in Complex Networks - Crossing the Land of Tinkering (2002, background): “Recent research on a number biological, social and technological graphs revealed that they share a common feature: the so called small world (SW) property (Watts & Strogatz, 1998; Newman, 2000).”