@conference {27,
	title = {Predicting social ties in mobile phone networks},
	booktitle = {2010 IEEE International Conference on Intelligence and Security Informatics (ISI)},
	year = {2010},
	month = {05/2010},
	address = {Vancouver, BC, Canada},
	abstract = {<p>A social network dynamically changes since the social relationships (social ties) change over time. The evolution of a social network mainly depends on the evolution of the social relationships. The social-tie strengths of person-to-person are different one another even though they are in the same group. In this paper we investigate the evolution of person-to-person social relationships, quantify and predict social tie strengths based on call-detail records of mobile phones. We propose an affinity model for quantifying social-tie strengths in which a reciprocity index is integrated to measure the level of reciprocity between users and their communication partners. Since human social relationships change over time, we map the call-log data to time series of the social-tie strengths by the affinity model. Then we use ARIMA model to predict social-tie strengths. For validation of our results, we used actual call logs of 81 users collected for a period of 8 months at MIT by the Reality Mining Project group and also used call logs of 20 users collected for a period of 6 months by UNT{\textquoteright}s Network Security team. These users have around 5000 communication partners. The experimental results show that our model is effective. We achieve prediction performance with accuracy of average 95.2\% for socially close and near members. Among other applications, this work is useful for homeland security, detection of unwanted calls (e.g., spam), and marketing.</p>
},
	keywords = {ARIMA, Prediction, reciprocity index, social groups, Social networks, social relationships, Social-tie strength},
	isbn = {978-1-4244-6444-9},
	doi = {10.1109/ISI.2010.5484780},
	author = {Zhang, Huiqi and Ram Dantu}
}
