@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 = {<p>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.</p>
},
	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 {99,
	title = {Call Algebra},
	booktitle = {2008 5th IEEE Consumer Communications and Networking Conference},
	year = {2008},
	month = {Jan},
	abstract = {<p>In every day life, people communicate through a voice(e.g., VoIP) network with different social groups that range from known people such as family members, friends, and distant relatives to unknown people such as spammers, telemarketers, and phishers. We believe that there exists a human/social dynamics between individuals by the way calls are generated, handled and received. In this paper we present how this dynamics can be used for detecting and filtering unwanted calls. In this paper, we first enumerate the communication patterns between the called party (callee) and the calling parties (callers). Next, we discuss operations on caller-callee matrices constructed based on their communication patterns, and derive call-constructs that can be used for determining the legitimacy of the calls and the callers. Finally, we discuss how these communication patterns and operations can be grouped for solutions to few of the existing IP telephony problems. These solutions can complement the existing no-call-lists in voice networks.</p>
},
	keywords = {Algebra, call algebra, caller-callee matrices, communication pattern, Computer science, Context modeling, Filtering, Humans, Internet telephony, IP networks, IP telephony problem, matrix algebra, Mood, Peer to peer computing, social aspects of automation, social group, Social network services, voice-over-IP, VoIP network},
	doi = {10.1109/ccnc08.2007.199},
	author = {Kolan, Prakash and Ram Dantu}
}
