An analysis of social networks based on tera-scale telecommunication datasets

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buir.contributor.authorAksu, Hidayet
buir.contributor.authorKörpeoğlu, İbrahim
buir.contributor.authorUlusoy, Özgür
dc.citation.epage360en_US
dc.citation.issueNumber2en_US
dc.citation.spage349en_US
dc.citation.volumeNumber7en_US
dc.contributor.authorAksu, Hidayeten_US
dc.contributor.authorKörpeoğlu, İbrahimen_US
dc.contributor.authorUlusoy, Özgüren_US
dc.date.accessioned2020-02-05T07:55:00Z
dc.date.available2020-02-05T07:55:00Z
dc.date.issued2019
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractWith the popularization of mobile phone usage, telecommunication networks have turned into a socially binding medium. Considering the traces of human communication held inside these networks, telecommunication networks are now able to provide a proxy for human social networks. To study degree characteristics and structural properties in large-scale social networks, we gathered a tera-scale dataset of call detail records that contains ≈ 5 × 10 7 nodes and ≈ 3.6 × 10 10 links for three GSM (mobile) networks, as well as ≈ 1.4 × 10 7 nodes and ≈ 1.9 × 10 9 links for one PSTN (fixed-line) network. In this paper, we first empirically evaluate some statistical models against the degree distribution of the country's call graph and determine that a Pareto log-normal distribution provides the best fit, despite claims in the literature that power-law distribution is the best model. We then question how network operator, size, density, and location affect degree distribution to understand the parameters governing it in social networks. Our empirical analysis indicates that changes in density, operator and location do not show a particular correlation with degree distribution; however, the average degree of social networks is proportional to the logarithm of network size. We also report on the structural properties of the communication network. These novel results are useful for managing and planning communication networks.en_US
dc.identifier.doi10.1109/TETC.2016.2627034en_US
dc.identifier.issn2168-6750
dc.identifier.urihttp://hdl.handle.net/11693/53080
dc.language.isoEnglishen_US
dc.publisherIEEE Computer Societyen_US
dc.relation.isversionofhttps://dx.doi.org/10.1109/TETC.2016.2627034en_US
dc.source.titleIEEE Transactions on Emerging Topics in Computingen_US
dc.subjectSocial networksen_US
dc.subjectDegree analysisen_US
dc.subjectCall graphen_US
dc.subjectEmpirical analysisen_US
dc.subjectTera-scale dataseten_US
dc.titleAn analysis of social networks based on tera-scale telecommunication datasetsen_US
dc.typeArticleen_US
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