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Random graph models have often been used in order to model networks, and manage to reproduce some observations from real-world networks, such as the small diameter and the presence of a giant component, such as observed on mobile datasets.
Nanavati et al. [19] observed in the study of 4 mobile datasets that besides the power-law tail of the degree distribution, the degree of a node is strongly correlated with the degree of its neighbors.
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We analyze the mobile phone call logs and human mobility traces from a large-scale mobile dataset collected in Côte d'Ivoire with the aim to further understand the country's economic development.
Results of our model for the Facebook data are presented in Table 4 where we can see a qualitative match with the ones for the mobile dataset, although the predictive power of the models is smaller than in that case.
Another shortcoming of this area of research comes from the current difficulty of gaining access to those mobile phone datasets, especially to cross-border mobility.
Those same trends have also been observed in later studies of mobile phone datasets [41].
The communication networks obtained by using mobile phone datasets have drawn increasing attention in recent years.
The recent availability of mobile phone datasets have led to many discoveries on human behavior.
The number of possible research questions on mobile phone datasets is gigantic.
In this paper, we review some advances made recently in the study of mobile phone datasets.
The study of mobile phone datasets is part of an emerging field known as computational social science [36].
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com