Identity and Search in Social Networks

By Duncan J. Watts, Peter Sheridan Dodds, Mark Newman. Science, 2002

1,002 citations recorded by the source corpus.

Social networks have the surprising property of being “searchable”: Ordinary people are capable of directing messages through their network of acquaintances to reach a specific but distant target person in only a few steps. We present a model that offers an explanation of social network searchability in terms of recognizable personal identities: sets of characteristics measured along a number of social dimensions. Our model defines a class of searchable networks and a method for searching them that may be applicable to many network search problems, including the location of data files in peer-to-peer networks, pages on the World Wide Web, and information in distributed databases.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/0b1d537ed9ee316fecea3f43aeebb6c117215974

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In Management Science and Operations Research

In Statistical and Nonlinear Physics

Citations in the corpus

The “New” Science of Networks (2004, background): “And second, Watts et al. (2002) have proposed a model of generalized afﬁliation networks in which distance between groups is deﬁned according to some number of social dimensions (e.g., geography and occupation), and individuals are characterized by the coordinates of the groups probability that…”
Uncovering space-independent communities in spatial networks (2010, background): “More-over, both aspects can be seen as originating from proximity in a high-dimensional social space, which summarizes people’s interests and characteristics, i.e. nodes have a tendency to connect with neighbouring nodes in social space [55].”
The Structure and Function of Complex Networks (2003, background): “[415] and independently by Kleinberg [240].”
A "Small-World" Network Model of Cognitive Insight (2005, background): “Watts, Dodds, and Newman (2002) modeled searchable networks by creating identity vectors for each node that represent sets of characteristics measured along a number of dimensions.”
Uncovering the overlapping community structure of complex networks in nature and society (2005, background): “Identifying these a priori unknown building blocks (functionally related proteins [5, 6], industrial sectors [7], groups of people [8, 9], etc .) is crucial to the understanding of the structural and functional properties of networks.”
The convergence of social and technological networks (2008, background): “1 Here too, this rate approximated an analogue of the inverse-square law— in a form adapted to hierarchies 21, 39 —although the messages in the re-searchers’ data were skewed a bit more toward long-range contacts in the organization than short-range ones.”
Navigable networks as Nash equilibria of navigation games (2014, result): “This is fully in line with the edge-locality principle of complex networks [68] [69] [70] which many times accounted for the high clustering coefficient.”
How to search a social network (2003, methodology): “92, is in the searchable range of the Watts model [13]”