@inbook {91, title = {Event Detection Based on Call Detail Records}, booktitle = {Behavior Computing: Modeling, Analysis, Mining and Decision}, year = {2012}, pages = {305{\textendash}316}, publisher = {Springer London}, organization = {Springer London}, address = {London}, isbn = {978-1-4471-2969-1}, doi = {10.1007/978-1-4471-2969-1_19}, url = {http://dx.doi.org/10.1007/978-1-4471-2969-1_19}, author = {Zhang, Huiqi and Ram Dantu}, editor = {Cao, Longbing and Yu, Philip} } @inbook {93, title = {Smart Phone: Predicting the Next Call}, booktitle = {Behavior Computing: Modeling, Analysis, Mining and Decision}, year = {2012}, pages = {317{\textendash}325}, publisher = {Springer London}, organization = {Springer London}, address = {London}, abstract = {

Prediction of incoming calls can be useful in many applications such as social networks, (personal, business) calendar and avoiding voice spam. Predicting incoming calls using just the context is a challenging task. We believe that this is a new area of research in context-aware ambient intelligence. In this paper, we propose a call prediction scheme and investigate prediction based on callers{\textquoteright} behavior and history. We present Holt-Winters method to predict calls from frequent and periodic callers. The Holt-Winters method shows high accuracy. Prediction and efficient scheduling of calls can improve the security, productivity and ultimately the quality of life.

}, isbn = {978-1-4471-2969-1}, doi = {10.1007/978-1-4471-2969-1_20}, url = {http://dx.doi.org/10.1007/978-1-4471-2969-1_20}, author = {Zhang, Huiqi and Ram Dantu}, editor = {Cao, Longbing and Yu, Philip} } @article {282, title = {Socioscope: Human Relationship and Behavior Analysis in Social Networks}, journal = {IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans}, volume = {41}, year = {2011}, month = {Nov}, pages = {1122-1143}, abstract = {

In this paper, we propose a socioscope model for social-network and human-behavior analysis based on mobile-phone call-detail records. Because of the diversity and complexity of human social behavior, no one technique will detect every attribute that arises when humans engage in social behaviors. We use multiple probability and statistical methods for quantifying social groups, relationships, and communication patterns and for detecting human-behavior changes. We propose a new index to measure the level of reciprocity between users and their communication partners. This reciprocity index has application in homeland security, detection of unwanted calls (e.g., spam), telecommunication presence, and product marketing. For the validation of our results, we used real-life call logs of 81 users which contain approximately 500 000 h of data on users{\textquoteright} location, communication, and device-usage behavior collected over eight months at the Massachusetts Institute of Technology (MIT) by the Reality Mining Project group. Also, call logs of 20 users collected over six months by the University of North Texas (UNT) Network Security team are used. The MIT and UNT data sets contain approximately 5000 callers. The experimental results show that our model is effective.

}, keywords = {behavioural sciences computing, Change points, communication pattern, Data models, human relationship analysis, human social behavior, human-behavior change detection, Humans, IEEE, Massachusetts Institute of Technology, Mobile handsets, probability, Reality Mining Project group, reciprocity index, Social factors, social group, social groups, social network analysis, Social network services, social networking (online), Social networks, social relationship, social relationships, social sciences computing, socioscope, Statistical analysis, statistical method, University of North Texas}, issn = {1083-4427}, doi = {10.1109/TSMCA.2011.2113335}, author = {Zhang, Huiqi and Ram Dantu and Cangussu, Jo{\~a}o} } @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 = {

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.

}, 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} } @mastersthesis {236, title = {Socioscope: Human Relationship and Behavior Analysis in Mobile Social Networks}, year = {2010}, school = {University of North Texas}, type = {phd}, address = {Denton, TX, USA}, abstract = {

The widely used mobile phone, as well as its related technologies had opened opportunities for a complete change on how people interact and build relationship across geographic and time considerations. The convenience of instant communication by mobile phones that broke the barrier of space and time is evidently the key motivational point on why such technologies so important in people{\textquoteright}s life and daily activities. Mobile phones have become the most popular communication tools.

Mobile phone technology is apparently changing our relationship to each other in our work and lives. The impact of new technologies on people{\textquoteright}s lives in social spaces gives us the chance to rethink the possibilities of technologies in social interaction. Accordingly, mobile phones are basically changing social relations in ways that are intricate to measure with any precision.

In this dissertation I propose a socioscope model for social network, relationship and human behavior analysis based on mobile phone call detail records. Because of the diversities and complexities of human social behavior, one technique cannot detect different features of human social behaviors. Therefore I use multiple probability and statistical methods for quantifying social groups, relationships and communication patterns, for predicting social tie strengths and for detecting human behavior changes and unusual consumption events. I propose a new\ reciprocity index\ to measure the level of reciprocity between users and their communication partners. The experimental results show that this approach is effective. Among other applications, this work is useful for homeland security, detection of unwanted calls (e.g., spam), telecommunication presence, and marketing. In my future work I plan to analyze and study the social network dynamics and evolution.

}, isbn = {978-1-124-36491-9}, author = {Zhang, Huiqi and Ram Dantu} } @conference {59, title = {Change Point Detection Based on Call Detail Records}, booktitle = {Proceedings of the 2009 IEEE International Conference on Intelligence and Security Informatics}, year = {2009}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Piscataway, NJ, USA}, abstract = {

In this paper we propose a method for combining wavelet denoising and sequential approach for detecting change points on mobile phone based on detailed call records. The Minmax method is used to estimate the thresholds of frequency and call duration for denoising. This work is useful to enhance homeland security, detecting unwanted calls (e.g., spam) and commercial purposes. For validation of our results, we randomly choose actual call logs of 20 users from 100 users collected at MIT by the Reality Mining Project group for a period of 8 months. Simulation data is also used to validate the results. The experimental results show that our model achieves good performance with high accuracy.

}, isbn = {978-1-4244-4171-6}, url = {http://dl.acm.org/citation.cfm?id=1706428.1706438}, author = {Zhang, Huiqi and Ram Dantu and Cangussu, Jo{\~a}o} } @conference {83, title = {Opt-in Detection Based on Call Detail Records}, booktitle = {Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference}, year = {2009}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Las Vegas, NV, USA}, abstract = {

Opt-in phone calls or emails refer to promotional phone calls or emails that have been requested by the people receiving them. In this paper we propose a model based on dynamic sliding windows to detect opt-in phone calls based on mobile phone call detail records. This work is useful for detecting unwanted calls (e.g., spam) and commercial purposes. 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. The experimental results show that our model achieves good performance with 91\% accuracy.

}, isbn = {978-1-4244-2308-8}, url = {http://dl.acm.org/citation.cfm?id=1700527.1700690}, author = {Zhang, Huiqi and Ram Dantu} } @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 = {

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.

}, 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 {38, title = {Discovery of Social Groups Using Call Detail Records}, booktitle = {On the Move to Meaningful Internet Systems: OTM 2008 Workshops}, year = {2008}, month = {11/2008}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, address = {Monterrey, Mexico}, abstract = {

In this paper we propose the affinity model for classifying social groups based on mobile phone call detail records. We use affinity to measure the similarity between probability distributions. Since phone calls are stochastic process, it makes more sense to use probability affinity to classify the social groups. This work is useful for enhancing homeland security, 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. The experimental results show that our model achieves good performance with high accuracy.

}, isbn = {978-3-540-88875-8}, doi = {10.1007/978-3-540-88875-8_72}, url = {http://dx.doi.org/10.1007/978-3-540-88875-8_72}, author = {Zhang, Huiqi and Ram Dantu}, editor = {Meersman, Robert and Tari, Zahir and Herrero, Pilar} } @conference {56, title = {Quantifying the Presence of Phone Users}, booktitle = {2008 5th IEEE Consumer Communications and Networking Conference}, year = {2008}, month = {Jan}, abstract = {

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.

}, 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} }