Geographic routing in social networks

By David Liben-Nowell, Jasmine Novak, Ravi Kumar, Prabhakar Raghavan, Andrew Tomkins. Proc. Natl. Acad. Sci. USA, 2005

890 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/fea26e04d89a7537d5d067f8dcb118b1937d60da

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In Management Science and Operations Research

In Statistical and Nonlinear Physics

Citations in the corpus

To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles (2009, positive): “[12] showed that most of the friendship links in LiveJournal are related to geographical proximity.”
Spatial Networks (2010, background): “The apparent paradox is thus solved: for the social network studied in [217], we observe R ∼ d which explains P (d) ∼ 1/d and the network is still navigable.”
Unsupervised Pivot Translation for Distant Languages (2019, background): “Generally speaking, routing can be performed in many types of networks, including circuit switching network (Girard, 1990), computer networks (e.g., Internet) (Huitema, 2000), transportation networks (Raff, 1983) and social networks (Liben-Nowell et al., 2005).”
Human mobility: Models and applications (2017, background): “The parameter pc represents social relations that emerge online, inspired by the observations made by [116].”
On compressing social networks (2009, background): “[18] showed that about the 70% of social network links arise from geographical proximity, suggesting that friends can be grouped together using geographical information.”
Geography of Twitter networks (2012, background): “Similar to Twitter ties, LiveJournal " friendship " ties can also be traced, and have in fact been studied, for example, by Liben-Nowell et al. (2005) who found that distance has an effect.”
Supervised Text-based Geolocation Using Language Models on an Adaptive Grid (2012, background): “However, Liben-Nowell et al. (2005) show that roughly 70% of social network links can be described using geographic information and that the probability of a social link is inversely proportional to geographic distance.”
Analysis of topological characteristics of huge online social networking services (2007, background): “Another work investigates an online blog community, LiveJour-nal [9].”
Community detection in networks: A user guide (2016, background): “For instance, if we take social networks, we can assume that the probability that two individuals know each other is a decreasing function of their geographical distance, on average (Liben-Nowell et al., 2005).”
Social Structure of Facebook Networks (2011, neutral): “…Materials, Nanoscience & Technology, University of North Carolina, Chapel Hill, NC 27599-3216, USA e Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, OX1 3LB, UK f CABDyN Complexity Centre, University of Oxford, OX1 1HB, UK a r t i c l e i n f o”
The Anatomy of the Facebook Social Graph (2011, background): “ same country, but that friendships between countries are also highly modular, and apparently inﬂuenced by geography. The inﬂuence of geographical distance on friendship has been previously discussed [41,42], but earlier work on Facebook has not examined the country-scale structure of friendships [4”
Wherefore Art Thou R3579x? Anonymized Social Networks, Hidden Patterns, and Structural Steganography (background): “While a number of these on-line data sources are based on publicly crawlable blogging and social networking sites [6] [20] [22], where users have explicitly chosen to publish their links to others, many of the most promising opportunities for the study of social networks are emerging from data on…”
A Tale of Many Cities: Universal Patterns in Human Urban Mobility (2011, background): “There is an almost uniform probability of traveling in the ﬁrst 100 meters, that is followed by a decreasing trend between 100 meters and a distance threshold δ m ∈ [5 , 30] km, where we detect an abrupt cutoﬀ in the probability of observing a human transition.”
Searching for superspreaders of information in real-world social media (2014, result): “Previous research has shown that this network has characteristics consistent with other large-scale social networks 46,47 .”
Four degrees of separation (2011, background): “6Incidentally, there have been several attempts to reproduce Milgram-like routing experiments on various large networks [17, 13, 10], an independent line of research that is interesting in its own right, but we do not explore the routing properties of Facebook in this study.”