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On the other side, a great effort has been devoted to study the local properties of mobile phone graphs.
Generally, the percentages of nodes in the giant components are comparable with other mobile phone graphs [5], and the SMS graph is less connected w.r.t. the call graph.
The topic of dynamical - or temporal - networks has been studied broadly regarding several types of networks [71], but the study of mobile phone graphs as evolving ones is rather recent, and given their inherent dynamical nature, mobile call graphs are excellent sources of information for such studies.
For instance, the structural properties of the mobile phone graphs have been investigated by Nanavati et al. [5] and by Onnela et al. [3], whose studies represent the first attempt to analyze large social networks as they emerge from mobile communications.
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In our analysis we have considered 100,000 random ties from the remaining reciprocated links of the mobile phone graph that have some activity in Ω.
Cebrian et al. [141] studied this with a small model, where each node of a mobile phone graph is represented as an agent assorted with a state represented by a binary string.
Dong et al. report interesting results in predicting user's gender and age in a mobile phone social graph using a double-dependent Factor Graph model [43].
It is happening now because the powerful combination of the mobile phone, the social graph, and new authentication technologies is reducing the cost of using social capital effectively at a transaction level.
He also alluded to two new products that have yet to gain traction with Facebook users: Home, the company's mobile phone software, and Graph Search.
By tying remittance to the social graph of your mobile phone it turns anyone in your phone's contacts into a potential Azimo recipient and user.
The solution: connect the NASDAQ button to a mobile phone logged into Facebook to generate an open graph action.
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