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However, it quickly appeared that the so-called mobile call graphs (MCG) were structurally different from other complex networks, such as the web and internet, and deserved particular attention, see Figure 1 for an example of snowball sampling of a mobile phone network.
Moreover, the particular structure of mobile call graphs induces some issues for traditional community detection methods.
However, on large mobile call graphs involving millions of users, such clustering techniques are outplayed by community detection algorithms.
Mobile call graphs contain many small tree-like structures, which are badly handled by most community detection methods.
Tibely et al. [31] show that even though some community detection methods perform well on benchmark networks, they do not produce clear community structures on mobile call graphs.
Recalling that mobile call graphs show high clustering coefficient, and thus are locally dense, one can differentiate links based on their position in the network.
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Karsai et al. studied the implications of the bursty patterns on the links of a mobile call graph [90].
A particularity of a mobile call graph is that the links are very precisely located in time.
Proof is made by the study of the linguistic distribution of communities in a Belgian mobile call graph [32], where the communities returned by the Louvain method strikingly show a well-known linguistic split, as illustrated on Figure 5. Figure 5 Community detection in Belgium.
Going one step further, instead of inferring information on the nodes of the mobile calling graph, Motahari et al. study the difference in calling behavior depending on the relationship between two subscribers, characterizing different types of links.
They introduce an indicator named CallRank, obtained by running the weighted PageRank algorithm on an aggregated mobile calling graph of Ivory Coast, where nodes are the antennas and the weight of the links are the number of calls between each pair of antenna.
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