Geographical dispersal of mobile communication networks

By Renaud Lambiotte, Vincent D. Blondel, C. D. Kerchove, Etienne Huens, C. Prieur, Zbigniew Smoreda, P. Dooren. 2008

435 citations recorded by the source corpus.

In this paper, we analyze statistical properties of a communication network constructed from the records of a mobile phone company. The network consists of 2.5 million customers that have placed 810 million communications (phone calls and text messages) over a period of 6 months and for whom we have geographical home localization information. It is shown that the degree distribution in this network has a power-law degree distribution k−5 and that the probability that two customers are connected by a link follows a gravity model, i.e. decreases as d−2, where d is the distance between the customers. We also consider the geographical extension of communication triangles and we show that communication triangles are not only composed of geographically adjacent nodes but that they may extend over large distances. This last property is not captured by the existing models of geographical networks and in a last section we propose a new model that reproduces the observed property. Our model, which is based on the migration and on the local adaptation of agents, is then studied analytically and the resulting predictions are confirmed by computer simulations.

Subfields: Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/0ae135a2cf71608f51cb54f4e88f69ef7b607eb7

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In Statistical and Nonlinear Physics

In Transportation

Citations in the corpus

Geographic Constraints on Social Network Groups (2010, background): “The structure of dyadic social interactions is known to depend on geography, for example, as shown by the decay of friendship probability with distance, based on voluntary self-reports of hometown and US state, in a blog community [11], and the decrease in communication probability with distance bas”
Uncovering space-independent communities in spatial networks (2010, background): “It has been shown for systems as diverse as the International Trade Market (16), human migration (17), traffic flows (18), or mobile communication between cities (19, 20) that the volume of interaction between distant locations is successfully modeled by”
Friendship and mobility: user movement in location-based social networks (2011, background): “Recently, however, location-based online social networking applications have emerged, where users share their current location by checking-in on websites such as Foursquare, Facebook, Gowalla, etc.”
Ranges of human mobility in Los Angeles and New York (2011, background): “analyze statistical properties of social networks constructed from the records of a mobile phone company [18].”
Collective Response of Human Populations to Large-Scale Emergencies (2011, neutral): “It provides the time and duration of each mobile phone call [7], together with information on the tower that handled the call, thus capturing the real-time locations of the users [3,6,25] (Methods, File S1, Fig.”
Spatial Science and Network Science: Review and Outcomes of a Complex Relationship (2014, background): “Lambiotte et al. (2008) have argued, however, that although spatial networks validate the gravity model, they are also made of crucial long-distance links deforming the predictions.”
Understanding the Spreading Patterns of Mobile Phone Viruses (2009, background): “Thus to quantitatively study the spreading dynamics of mobile viruses we need to simultaneously model the location (13), the mobility (14-17) and the communication patterns (18-21) of mobile phone users.”
Measuring Large-Scale Social Networks with High Resolution (2014, background): “that the probability of the existence of the links decreases as d, where d is the distance between users [47].”
Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network (2013, background): “The increasing availability of digital traces of human behavior revealed that various human activities, e.g. mobility patterns, phone calls or email communication, are often characterized by heterogeneity [35–38].”
Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, background): “…patterns (Gonz´alez et al., 2008; Isaacman et al., 2010), the use of urban spaces (Reades et al., 2007), travel demand during special events (Calabrese et al., 2010), social network structure (Onnela et al., 2007) and geographical dispersal of mobile communications (Lambiotte et al., 2008).”
Link Prediction in Complex Networks: A Survey (2010, background): “Geographical distance is considered to be one of the realistic factors that aﬀect the existence of nodes’ interactions in networks [140].”
Persistence of social signatures in human communication (2012, background): “[14] Lambiotte R, Blondel V, de Kerchove C, Huens E, Prieur C, Smoreda Z, Van Dooren P (2008) Geographical dispersal of mobile communication networks.”
Smart cities of the future (2012, positive): “Distance decay in spatial interaction processes is a well-known phenomenon, and many recent studies on mobile phone data sets have confirmed distance decay relationships: the likelihood that two individuals are connected decreases with distance between them [30].”