@conference {41,
	title = {Quantifying Reciprocity in Social Networks},
	booktitle = {2009 International Conference on Computational Science and Engineering},
	year = {2009},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	address = {Washington, DC, USA},
	abstract = {<p>In this paper we propose a new reciprocity index for quantifying social relationships based on mobile phone call detail records and Twitter blogs. We use this reciprocity index to measure the level of reciprocity between users. This work is useful for detecting unwanted calls (e.g., spam) and product marketing. For validation of our results, we used actual call logs of 100 users collected at MIT by the Reality Mining Project group for a period of 8 months and Twitter blogs of 460 users collected by the Network Security team at UNT for a period of 12 months. The experimental results show that our model achieves results with high accuracy.</p>
},
	isbn = {978-0-7695-3823-5},
	doi = {10.1109/CSE.2009.399},
	url = {http://dx.doi.org/10.1109/CSE.2009.399},
	author = {Zhang, Huiqi and Ram Dantu and Cangussu, Jo{\~a}o}
}
@conference {56,
	title = {Quantifying the Presence of Phone Users},
	booktitle = {2008 5th IEEE Consumer Communications and Networking Conference},
	year = {2008},
	month = {Jan},
	abstract = {<p>Presence-enabled telephony services can reduce telephone tag and improve customer satisfaction. In this paper we proposed the Bayesian inference model to calculate the willingness level of the callee to accept calls. Before making a call, the caller may use the willingness calculator to find out whether the callee is available. Based on this level the user can make a decision whether to make a call. For validation of our results, we used actual call logs of 100 users collected at MIT by the Reality Mining Project group for a period of 8 months. We used time of the day, day of the week, talk-time and location for calculating the willingness level Our results show a good agreement between computed willingness level and the number of missed/rejected calls. This service can be included as part of the presence server. When deployed, this service can increase productivity, avoid unwanted calls and reduce the call traffic congestion. This service is beneficial to both subscribers and phone service providers. However, in order to make this service a reality, we need to take into account other factors such social closeness, proximity, multiplexity and reputation of the caller.</p>
},
	keywords = {Bayes methods, Bayesian inference model, Bayesian methods, call traffic congestion, Computer science, Context, customer satisfaction, Multimedia communication, Multimedia systems, Network servers, phone service provider, presence-enabled telephony service, Productivity, Roads, telecommunication congestion control, telecommunication traffic, telephony, willingness calculator},
	doi = {10.1109/ccnc08.2007.206},
	author = {Zhang, Huiqi and Ram Dantu}
}
