Abstract
Regular services in telecommunications produce massive volumes of relational data. In this work the data produced in telecommunications is seen as a streaming network, where clients are the nodes and phone calls are the edges. Visualization techniques are required for exploratory data analysis and event detection. In social network visualization and analysis the goal is to get more information from the data taking into account actors at the individual level. Previous methods relied on aggregating communities, k-Core decompositions and matrix feature representations to visualize and analyse the massive network data. Our contribution is a group visualization and analysis technique of influential actors in the network by sampling the full network with a top-k representation of the network data stream.
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Acknowledgments
This work was supported by Sibila and Smartgrids research projects (NORTE-07-0124-FEDER-000056/59), financed by North Portugal Regional Operational Programme (ON.2 O Novo Norte), under the National Strategic Reference Framework (NSRF), through the Development Fund (ERDF), and by national funds, through the Portuguese funding agency, Fundação para a Ciência e a Tecnologia (FCT), and by European Commission through the project MAESTRA (Grant number ICT-2013-612944). The authors also acknowledge the financial support given by the project number 18450 through the “SI I&DT Individual” program by QREN and delivered to WeDo Business Assurance. Finally the authors acknowledge the reviewers for their constructive reviews on this paper.
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Sarmento, R., Cordeiro, M., Gama, J. (2015). Visualization for Streaming Telecommunications Networks. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2014. Lecture Notes in Computer Science(), vol 8983. Springer, Cham. https://doi.org/10.1007/978-3-319-17876-9_8
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