M.I. Akbas, Raghu Avula, M. Bassiouni, and D. Turgut

Social Network Generation and Friend Ranking Based on Mobile Phone Data


Cite as:

M.I. Akbas, Raghu Avula, M. Bassiouni, and D. Turgut. Social Network Generation and Friend Ranking Based on Mobile Phone Data. In Proceedings of IEEE ICC'13, pp. 1444–1448, June 2013.

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Abstract:

Social networking websites have been increasingly popular in the recent years. The users create and maintain their social networks by themselves in these websites by establishing or removing the connections to friends and sites of interests. The smart phones not only create a high availability for social network applications, but also serve for all forms of digital communication such as voice or video calls, e-mails and texts, which are also the ways to form or maintain our social network. In this paper, we deal with the problem of automatically generating and organizing social networks by analyzing and assessing mobile phone usage and interaction data. We assign weights to the different types of interactions. The interactions among users are then evaluated based on these weight values for certain periods of time. We use these values to rank the friends of users by a sports ranking algorithm, which recognizes the changes in the collected data over time.

BibTeX:

@inproceedings{Akbas-2013-ICC,
   author = "M.I. Akbas and Raghu Avula and M. Bassiouni and D. Turgut",
   title = "Social Network Generation and Friend Ranking Based on Mobile Phone Data",
   booktitle = "Proceedings of IEEE ICC'13",
   pages = "1444-1448",
   month = "June",
   year = "2013",
   abstract = {Social networking websites have been increasingly popular in the recent years. The users create and maintain their social networks by themselves in these websites by establishing or removing the connections to friends and sites of interests. The smart phones not only create a high availability for social network applications, but also serve for all forms of digital communication such as voice or video calls, e-mails and texts, which are also the ways to form or maintain our social network. In this paper, we deal with the problem of automatically generating and organizing social networks by analyzing and assessing mobile phone usage and interaction data. We assign weights to the different types of interactions. The interactions among users are then evaluated based on these weight values for certain periods of time. We use these values to rank the friends of users by a sports ranking algorithm, which recognizes the changes in the collected data over time.  },
}

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