Calling, texting, and moving: multidimensional interactions of mobile phone users

Abstract

The communication networks obtained by using mobile phone datasets have drawn increasing attention in recent years. Studies have led to important advances in understanding the behavior of mobile users although they have just considered text message (short message service (SMS)), call data, and spatial proximity, separately. However, there is a growing awareness that human sociality is expressed simultaneously on multiple layers, each corresponding to a specific way an individual has to communicate. In fact, besides the common real life encounters, a mobile phone user has at least two further communication media to exploit, SMSs and voice calls. This is advocating a multidimensional approach if we are seeking a compound description of the human mobile social behavior. In this context, we perform the first study of the multiplex mobile network, gathered from the records of both call and text message activities, along with relevant geographical information, of millions of users of a large mobile phone operator over a period of 12 weeks. By computing a set of complex network metrics, at different scales, onto the three single layers given by calls, SMSs and spatial proximity, and their extensions onto a three-level network, we provide a comprehensive study of the global multi-layered network which arises from both the overall on-the-phone communications performed by mobile users and their spatial propinquity.

Quantifying the differences incall detail records

“…Indeed, differences in interactions generated by our use of mobile phones have already been found in previous studies [30]. Differences and similarities in SMS and phone call data have been studied using a multi-layered network approach to show that mobile phone data may be better modelled by a network of communication channels [31]. Additionally, communications via mobile phones tend to happen between people who are more likely to be near each other [31], suggesting the existence of strong spatial interactions in mobile phone data.…”

“…Differences and similarities in SMS and phone call data have been studied using a multi-layered network approach to show that mobile phone data may be better modelled by a network of communication channels [31]. Additionally, communications via mobile phones tend to happen between people who are more likely to be near each other [31], suggesting the existence of strong spatial interactions in mobile phone data. Ego-networks of mobile phone interactions have been quantified in terms of social signatures , which is a way of measuring how each individual allocates their interactions across the network [32].…”

Urban groups: behavior and dynamics of social groups in urban space

“…The identification of cohesive groups, which is a central problem in both graph theory and social network analysis, entails different methods-from community detection [25,29] to enumeration of particular maximal subgraphs [23,30]. In this work we focus on the latter approach since community detection methods, when applied to this phone graph, have been shown to return loosely connected subgraphs barely interpretable as groups or tight-knit communities [31]. In fact, the communities detected by different algorithms are characterized by an average density which varies from 0.019 (Louvain algorithm [25]) to 0.35 (Leung's algorithm [32]).…”

“…To assess whether the emergence of the urban groups is not only due to the well-known correlation between the on-phone interactions and co-location which characterizes the reciprocal calls between pairs of users [15,36], we test if the measured number of groups is significantly higher than the one obtained by a null model, in which a dependency between communications and co-location exists. Specifically, the null model is based on the co-location graph studied in our previous work [31] and on the observation that, given a link between two customers in the co-location graph, the probability that they communicate by call or text is 0.06. So, for each link in the co-location graph we draw the corresponding link in the interaction one with probability 0.06, then we extract the quasi-cliques.…”

Walls-in-one: usage and temporal patterns in a social media aggregator

“…To this aim we move within the multidimensional or multiplex network theory Magnani and Rossi (2011) ; Bródka et al (2012 ); Berlingerio et al (2012) ; Kivelä et al (2014 ); D’AGostino and Scala (2014 ) since it provides the tools and the models which better capture and measure the interplay/correlation among the social sites users adopt. Specifically the scenario we are dealing with is well represented by a multigraph Zignani et al ( 2015 ), a special case of an heterogeneous information network Sun and Han (2012) . So far multiplex network theory has been applied to different real case studies; from citation Li et al (2016a ), co-author Sun et al (2011) and conference-author networks Sun et al (2009) to power grids Brummitt et al (2012) , economic Li et al (2016b ); Li et al (2014) ; Barigozzi et al (2011) and biological networks Bauch and Galvani (2013) .…”