Analysis of the communities of an urban mobile phone network

Botta, Federico; Genio, Charo I. del

doi:10.1371/journal.pone.0174198

Cited by 20 publications

(9 citation statements)

References 40 publications

(24 reference statements)

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“…Since most of those constrains are common to human nature, those patterns show also a large degree of universality across individuals. Interestingly, deviations from universal rhythms can inform us about changes of behavior related to, for example, unemployment [22], health conditions [23], or crowd events [4,8]. Along this line, our research also shows that future network dynamics is encoded in the relative properties of the temporal patterns of communication between individuals and that those temporal properties have more predicting power than structural, intensity or intimacy features of the communication.…”

Section: Discussionsupporting

confidence: 55%

Temporal patterns behind the strength of persistent ties

et al. 2017

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Social networks are made out of strong and weak ties having very different structural and dynamical properties. But what features of human interaction build a strong tie? Here we approach this question from a practical way by finding what are the properties of social interactions that make ties more persistent and thus stronger to maintain social interactions in the future. 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. While our results confirm that structural (embeddedness) and intensity (number of calls) features are correlated with tie persistence, temporal features of communication events are better and more efficient predictors for tie persistence. Specifically, although communication within ties is always bursty we find that ties that are more bursty than the average are more likely to decay, signaling that tie strength is not only reflected in the intensity or topology of the network, but also on how individuals distribute time or attention across their relationships. We also found that stable relationships have and require a constant rhythm and if communication is halted for more than 8 times the previous communication frequency, most likely the tie will decay. Our results not only are important to understand the strength of social relationships but also to unveil the entanglement between the different temporal scales in networks, from microscopic tie burstiness and rhythm to macroscopic network evolution.

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Section: Discussionsupporting

confidence: 55%

Temporal patterns behind the strength of persistent ties

et al. 2017

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“…[3], [4], [5], [6], [7], [8]. Some emphasizes on the importance of temporal patterns of communications between people as the evolution of communities and social groups [9], other uses the CDR data to map real population estimates in time and space [10], or explore patterns of international/migrant communities in a global city [11].…”

Section: Related Workmentioning

confidence: 99%

Can Temperature Be Used as a Predictor of Data Traffic? A Real Network Big Data Analysis

Rafiq¹,

Farooq

Zoha

et al. 2018

2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT)

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The proliferation of mobile devices and big data has made it possible to understand the human movements and forecasts of precise and intelligent short and long-term data consumption of services like call, sms, or internet data which has interesting and promising applications in modern cellular networks. Human nature and moods are known to be synonymous with the physical attributes of mother nature such as temperature. The change in those physical features affects the human routines and activities such as cellular data consumptions. The future of telecommunication lies in the exploration of heap of information and data available to companies and inferring the valuable results through extensive analysis. In this paper, we analyze three main traits of cellular activity: sms, call, and internet. This paper investigates whether the relationship between the temperature and the cellular data consumption exits or not. This work introduces a novel approach to identify the strength of relationship between the temperature and cellular activity (sms, call, internet) and discuss the methods to quantify the relationship using correlation method. The real network CDR big data set-Milano Grid data set is used to analyze the behavior of the cellular activity with respect to temperature.

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“…An overview of empirical results on the multi-scale dynamics of social dynamics and networks inferred from CDRs is presented in [27]. In [28], a study of the community structure of a mobile phone call network is presented and its evolution over time is discussed, revealing the temporal patterns in local communications. A user-centric grid-based approach that utilizes location information retrieved from subscriber CDRs as a basis for cell parameter optimization is presented in [29].…”

Section: Call Detail Records Approachesmentioning

confidence: 99%

Real-Time Recognition of Calling Pattern and Behaviour of Mobile Phone Users through Anomaly Detection and Dynamically-Evolving Clustering

et al. 2017

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Abstract:In the competitive telecommunications market, the information that the mobile telecom operators can obtain by regularly analysing their massive stored call logs, is of great interest. Although the data that can be extracted nowadays from mobile phones have been enriched with much information, the data solely from the call logs can give us vital information about the customers. This information is usually related with the calling behaviour of their customers and it can be used to manage them. However, the analysis of these data is normally very complex because of the vast data stream to analyse. Thus, efficient data mining techniques need to be used for this purpose. In this paper, a novel approach to analyse call detail records (CDR) is proposed, with the main goal to extract and cluster different calling patterns or behaviours, and to detect outliers. The main novelty of this approach is that it works in real-time using an evolving and recursive framework.

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Analysis of the communities of an urban mobile phone network

Cited by 20 publications

References 40 publications

Temporal patterns behind the strength of persistent ties

Temporal patterns behind the strength of persistent ties

Can Temperature Be Used as a Predictor of Data Traffic? A Real Network Big Data Analysis

Real-Time Recognition of Calling Pattern and Behaviour of Mobile Phone Users through Anomaly Detection and Dynamically-Evolving Clustering

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