Random graph models of social networks

By Mark Newman, Duncan J. Watts, Steven H. Strogatz. Proceedings of the National Academy of Sciences of the United States of America, 2002

1,489 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/6b6ae4ff053bcee2834b5e7718810cb5bc15c36c

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

A Survey of Statistical Network Models (2009, background): “Physicists, on the other hand, tend to be interested in understanding parsimonious mechanisms for network formation [28; 235].”
A Core/Periphery Perspective on Individual Creative Performance: Social Networks and Cinematic Achievements in the Hollywood Film Industry (2008, background): “Data on affiliation networks tend to be more reliable than those on other social networks because group membership often can be determined with greater precision than in cases of friendship or other types of ties (Newman et al. 2002).”
Truss Decomposition in Massive Networks (2012, background): “Conceptually, the deﬁnition of k -truss is also more rigorous than that of k -core since k -truss is deﬁned based on triangles , which are known as fundamental building blocks of a network [32, 33, 25, 6].”
Event-based social networks: linking the online and offline social worlds (2012, background): “In [12], Feld proposed a focus theory in which individuals organize their social interactions around foci, such as workplaces, families, etc; whereas [20, 16, 3] utilized affiliation to explain the construction of social connections.”
Self-organized adaptation of a simple neural circuit enables complex robot behaviour (2010, background): “Nevertheless, as the gap sizes scale weakly algebraically with system size (2) with often small β such gaps may still be essentially macroscopic, that is, substantially large even formacroscopic systems of large finite sizeN .”
Controlling edge dynamics in complex networks (2011, background): “Research in network science has mostly been focused on measuring [6, 8, 62], modeling [28, 44] and decomposing [22, 43, 47] network representations of existing natural phenomena in order to deepen our understanding of the underlying systems.”
Competing Through Cooperation: The Organization of Standard Setting in Wireless Telecommunications (2008, background): “Technical consortia give rise to affiliation networks, also called bipartite networks (Wasserman and Faust 1994, Borgatti and Everett 1997, Newman et al. 2002).”
The emergent properties of a dolphin social network (2003, background): “Complex networks that contain many members such as human societies (Newman et al. 2002), the World Wide Web (WWW) (Lawrence & Giles 1999), or electric power grids (Watts & Strogatz 1998), have emergent properties that permit all components (or vertices) in the network to be linked by a short chain…”
Small Worlds and Regional Innovation (2007, neutral): “2000), the spread of fads or epidemics (Newman and Watts 1999), and the aggregation of isolated scientific collaborators (Newman et al. 2002).”
Adversarial Examples on Graph Data: Deep Insights into Attack and Defense (2019, methodology): “Graph is commonly used to model many real-world relationships, such as social networks [Newman et al. , 2002], citation networks and transactions [Ron and Shamir, 2013] and the control-ﬂow of programs [Allen, 1970].”
Small-World Phenomena and the Dynamics of Information (2001, background): “It is important to note that the tree T is used only in the generation process of G; neither the edges nor the non-leaf nodes of T appear in G. (By way of contrast, the lattice model in [15] included both the long-range links and the nearest-neighbor links of the lattice itself.)”
Indicators for Complex Innovation Systems (2006, background): “Power laws are common to physical (Christensen et al., 2002; Warhaft 2002), natural science (Goldberger et al., 2002; Katz et al., 1999; West et al., 2002) and social systems (M. E. J. Newman et al., 2002).”
Simplicial closure and higher-order link prediction (2018, background): “Prior research has also identified the distinction between open and closed triangles when projecting bipartite networks but has not studied the idea of simplicial closure events (7, 48).”