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Social Network Analysis Utilizing Big Data Technology
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Computing Science.
2012 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

As of late there has been an immense increase of data within modern society. This is evident within the field of telecommunications. The amount of mobile data is growing fast. For a telecommunication operator, this provides means of getting more information of specific subscribers. The applications of this are many, such as segmentation for marketing purposes or detection of churners, people about to switching operator. Thus the analysis and information extraction is of great value. An approach of this analysis is that of social network analysis. Utilizing such methods yields ways of finding the importance of each individual subscriber in the network.

This thesis aims at investigating the usefulness of social network analysis in telecommunication networks. As these networks can be very large the methods used to study them must scale linearly when the network size increases. Thus, an integral part of the study is to determine which social network analysis algorithms that have this scalability. Moreover, comparisons of software solutions are performed to find product suitable for these specific tasks.

Another important part of using social network analysis is to be able to interpret the results. This can be cumbersome without expert knowledge. For that reason, a complete process flow for finding influential subscribers in a telecommunication network has been developed. The flow uses input easily available to the telecommunication operator. In addition to using social network analysis, machine learning is employed to uncover what behavior is associated with influence and pinpointing subscribers behaving accordingly.

Place, publisher, year, edition, pages
2012. , 71 p.
Series
UPTEC F, ISSN 1401-5757 ; 12007
Keyword [en]
Social Network Analysis, Telecommunication Networks, Hadoop, Machine Learning
National Category
Computer Science
Identifiers
URN: urn:nbn:se:uu:diva-170926OAI: oai:DiVA.org:uu-170926DiVA: diva2:509757
External cooperation
Ericsson AB
Educational program
Master Programme in Engineering Physics
Uppsok
Technology
Supervisors
Examiners
Available from: 2012-03-15 Created: 2012-03-13 Last updated: 2012-03-15Bibliographically approved

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CiteExportLink to record
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  • apa
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