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Evolution Analysis of Call Ego-Networks

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Discovery Science (DS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9956))

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Abstract

With the realization of networks in many of the real world domains, research work in network science has gained much attention now-a-days. The real world interaction networks are exploited to gain insights into real world connections. One of the notion is to analyze how these networks grow and evolve. Most of the works rely upon the socio centric networks. The socio centric network comprises of several ego networks. How these ego networks evolve greatly influences the structure of network. In this work, we have analyzed the evolution of ego networks from a massive call network stream by using an extensive list of graph metrics. By doing this, we studied the evolution of structural properties of graph and related them with the real world user behaviors. We also proved the densification power law over the temporal call ego networks. Many of the evolving networks obey the densification power law and the number of edges increase as a function of time. Therefore, we discuss a sequential sampling method with forgetting factor to sample the evolving ego network stream. This method captures the most active and recent nodes from the network while preserving the tie strengths between them and maintaining the density of graph and decreasing redundancy.

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Acknowledgments

This work was partly supported by the European Commission through MAESTRA (ICT-2013-612944) and the Project TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020 is financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement. Shazia Tabassum is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project (POCI-01-0145-FEDER-006961), and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013. The authors also thank WeDo Business for providing the data.

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Authors and Affiliations

  1. LIAAD, Inesctec, University of Porto, Porto, Portugal

    Shazia Tabassum & João Gama

Authors
  1. Shazia Tabassum
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  2. João Gama
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Correspondence to Shazia Tabassum .

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Editors and Affiliations

  1. Campus Middelhe, M.G.103a, Universiteit Antwerpen Campus Middelhe, M.G.103a, Antwerp, Belgium

    Toon Calders

  2. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  3. Bari, Italy

    Donato Malerba

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Tabassum, S., Gama, J. (2016). Evolution Analysis of Call Ego-Networks. In: Calders, T., Ceci, M., Malerba, D. (eds) Discovery Science. DS 2016. Lecture Notes in Computer Science(), vol 9956. Springer, Cham. https://doi.org/10.1007/978-3-319-46307-0_14

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  • DOI: https://doi.org/10.1007/978-3-319-46307-0_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46306-3

  • Online ISBN: 978-3-319-46307-0

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