Small-World Phenomena and the Dynamics of Information

By Jon M. Kleinberg. Neural Information Processing Systems, 2001

456 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/1bed57282935012df51ad87970cd01b90c2fb27c

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In Computer Networks and Communications

In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

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.”
The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth (2005, background): “This relationship in turn motivates a unifying explanation for some previously observed behavioral phenomena of memory search, under the hypothesis that search processes exploit the structure of semantic networks in something like the way that search algorithms for information on the web can exploit”
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).”
A Survey of Statistical Network Models (2009, background): “Liben-Nowell and Kleinberg [198] develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a network, e.g., the WWW.”
The Structure and Function of Complex Networks (2003, methodology): “While this model is primarily a model of search on social networks (or possibly the Web [240]), Watts et al.”
A framework for analysis of dynamic social networks (2006, background): “The most common way to capture information on social interactions is a network [15, 16, 20, 27, 31].”
Geographic routing in social networks (2005, background): “The notion of adding links with probability inversely proportional to the number of closer candidates is implicit in Kleinberg’s work on his group-structure model [20], which is based on people’s membership in groups like organizations or neighborhoods.”
Extremal Properties of Random Structures (2003, unknown): “rved fit g−0.74 g−1 Fig.17.Probability of two individuals corresponding by email as a function of the size of the smallest organizational unit they both belong to. The optimum relationship derived in [27] is p ∼ g−1, g being the group size. The observed relationship is p ∼ g−3/4. and proximity (geog”
How to search a social network (2003, background): “The optimum relationship derived in (Kleinberg, 2001) is p ∼ g, g being the group size.”