@article {13, title = {Identification of leaders, lurkers, associates and spammers in a social network: context-dependent and context-independent approaches}, journal = {Social Network Analysis and Mining}, volume = {1}, year = {2011}, pages = {241{\textendash}254}, abstract = {

In this paper, we present two methods for classification of different social network actors (individuals or organizations) such as leaders (e.g., news groups), lurkers, spammers and close associates. The first method is a two-stage process with a fuzzy-set theoretic (FST) approach to evaluation of the strengths of network links (or equivalently, actor-actor relationships) followed by a simple linear classifier to separate the actor classes. Since this method uses a lot of contextual information including actor profiles, actor-actor tweet and reply frequencies, it may be termed as a context-dependent approach. To handle the situation of limited availability of actor data for learning network link strengths, we also present a second method that performs actor classification by matching their short-term (say, roughly 25\ days) tweet patterns with the generic tweet patterns of the prototype actors of different classes. Since little contextual information is used here, this can be called a context-independent approach. Our experimentation with over 500 randomly sampled records from a twitter database consists of 441,234 actors, 2,045,804 links, 6,481,900 tweets, and 2,312,927 total reply messages indicates that, in the context-independent analysis, a multilayer perceptron outperforms on both on classification accuracy and a new F-measure for classification performance, the Bayes classifier and Random Forest classifiers. However, as expected, the context-dependent analysis using link strengths evaluated using the FST approach in conjunction with some actor information reveals strong clustering of actor data based on their types, and hence can be considered as a superior approach when data available for training the system is abundant.

}, keywords = {Context dependent and context independent data analysis, Fuzzy logic, MLP, Naive Bayesian classifier, Random Forest, Social networks, Twitter}, issn = {1869-5469}, doi = {10.1007/s13278-011-0017-9}, url = {http://dx.doi.org/10.1007/s13278-011-0017-9}, author = {Fazeen, Mohamed and Ram Dantu and Guturu, Parthasarathy} } @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 {68, title = {Efficiency of Social Connection-based Routing in P2P VoIP Networks}, booktitle = {Proceedings of the 2Nd International Conference on COMmunication Systems and NETworks}, year = {2010}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Piscataway, NJ, USA}, keywords = {P2P routing, P2P VoIP systems, Social networks, social-network based routing}, isbn = {978-1-4244-5487-7}, url = {http://dl.acm.org/citation.cfm?id=1831443.1831488}, author = {Chandrasekaran, Vikram and Ram Dantu and Gupta, Neeraj and Yang, Xiaohui and Wijesekera, Duminda} } @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} }