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As opposed to other studies in social networks [19] our mobile phone database does not contain any information about the context and content of the call.
Using a large longitudinal mobile phone database we build a predictive model of tie persistence based on intensity, intimacy, structural and temporal patterns of social interaction.
Since the results on both databases are similar we discuss here only the mobile phone database and refer to the Methods section for further details about the Facebook database analysis.
The authors analyze the most common motifs present in a mobile phone database and find that the most common temporal motifs of three events involve only two nodes, and motifs that allow a causal hypothesis are more frequent than those that do not.
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For example, we consider (log w_{ij}) instead of (w_{ij}) since the distribution of number of calls per tie is heavy skewed in mobile phone databases [23].
We have arbitrarily taken from the AR-set (the mobile-phone database) some samples the different size on which we applied Rank-Sort-MDP(_{mathrm {REF}}).
A schematic of the two-way connectivity between mobile phone and project database obtained using EpiCollect and spatialepidemiology.net is shown in Figure 1.
In an e-mail, Artas Bartas, Bribespot's managing director, said it's theoretically possible to link a specific report to a specific phone, but that doing so would "require some considerable resources," like access to a mobile phone company's user database.
However, EpiCollect is designed for situations where multiple field workers submit data via mobile phone to a central database.
To demonstrate this further, Figure 4 shows the web interface for an illustrative dataset where data from each sampling point have been submitted via mobile phone to the project database by multiple field workers across Europe using EpiCollect.
Even in a remote village, a mother can simply report a birth to a local government notifier, who then enters the information via mobile phone to the central database.
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