@conference {17643,
	title = {Predicting Trust and Distrust in Social Networks},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)},
	year = {2011},
	month = {2011/10/09/11},
	pages = {418 - 424},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {As user-generated content and interactions have overtaken the web as the default mode of use, questions of whom and what to trust have become increasingly important. Fortunately, online social networks and social media have made it easy for users to indicate whom they trust and whom they do not. However, this does not solve the problem since each user is only likely to know a tiny fraction of other users, we must have methods for inferring trust - and distrust - between users who do not know one another. In this paper, we present a new method for computing both trust and distrust (i.e., positive and negative trust). We do this by combining an inference algorithm that relies on a probabilistic interpretation of trust based on random graphs with a modified spring-embedding algorithm. Our algorithm correctly classifies hidden trust edges as positive or negative with high accuracy. These results are useful in a wide range of social web applications where trust is important to user behavior and satisfaction.},
	keywords = {distrust prediction, Electronic publishing, Encyclopedias, graph theory, inference algorithm, Inference algorithms, inference mechanisms, Internet, negative trust, online social networks, positive trust, Prediction algorithms, probability, random graphs, security of data, social media, social networking (online), spring-embedding algorithm, Training, trust inference, trust probabilistic interpretation, user behavior, user satisfaction, user-generated content, user-generated interactions},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.56},
	author = {DuBois,T. and Golbeck,J. and Srinivasan, Aravind}
}
@conference {17653,
	title = {Rigorous Probabilistic Trust-Inference with Applications to Clustering},
	booktitle = {IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, 2009. WI-IAT {\textquoteright}09},
	volume = {1},
	year = {2009},
	month = {2009/09/15/18},
	pages = {655 - 658},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The World Wide Web has transformed into an environment where users both produce and consume information. In order to judge the validity of information, it is important to know how trustworthy its creator is. Since no individual can have direct knowledge of more than a small fraction of information authors, methods for inferring trust are needed. We propose a new trust inference scheme based on the idea that a trust network can be viewed as a random graph, and a chain of trust as a path in that graph. In addition to having an intuitive interpretation, our algorithm has several advantages, noteworthy among which is the creation of an inferred trust-metric space where the shorter the distance between two people, the higher their trust. Metric spaces have rigorous algorithms for clustering, visualization, and related problems, any of which is directly applicable to our results.},
	keywords = {Clustering algorithms, Conferences, Educational institutions, Extraterrestrial measurements, Inference algorithms, Intelligent agent, random graphs, Social network services, trust inferrence, Visualization, Voting, Web sites},
	isbn = {978-0-7695-3801-3},
	doi = {10.1109/WI-IAT.2009.109},
	author = {DuBois,Thomas and Golbeck,Jennifer and Srinivasan, Aravind}
}
@conference {17640,
	title = {Optimal design of signaling networks for Internet telephony},
	booktitle = {IEEE INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings},
	volume = {2},
	year = {2000},
	month = {2000///},
	pages = {707-716 vol.2 - 707-716 vol.2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present an approach for efficient design of a signaling network for a network of software switches supporting Internet telephony. While one may take an integer programming approach to solve this problem, it quickly becomes intractable even for modest-sized networks. Instead, our topology design uses random graphs that we show to be nearly optimal in cost, highly connected, and computationally efficient even for large networks. We then formulate a quadratic assignment problem (QAP) to map the abstract topology into the physical network to achieve optimal load balancing for given demand forecasts, which we solve using randomized heuristics. Numerical results on several example networks illustrate the performance and computational efficiency of our method. A graphical design tool has been developed based on our algorithms},
	keywords = {bandwidth allocation, Computational efficiency, Computer networks, Cost function, Demand forecasting, demand forecasts, graph theory, graphical design tool, Internet telephony, Linear programming, Load forecasting, Load management, Network topology, optimal design, optimal load balancing, optimisation, performance, quadratic assignment problem, random graphs, randomised algorithms, randomized heuristics, Signal design, signaling networks, Switches, telecommunication signalling, topology design},
	isbn = {0-7803-5880-5},
	doi = {10.1109/INFCOM.2000.832245},
	author = {Srinivasan, Aravind and Ramakrishnan,K. G and Kumaran,K. and Aravamudan,M. and Naqvi,S.}
}
@conference {17554,
	title = {Approximating hyper-rectangles: learning and pseudo-random sets},
	booktitle = {Proceedings of the twenty-ninth annual ACM symposium on Theory of computing},
	series = {STOC {\textquoteright}97},
	year = {1997},
	month = {1997///},
	pages = {314 - 323},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	keywords = {approximations of distributions, derandomization, explicit constructions, machine learning, multiple-instance learning, PAC learning, pseudorandomness, Ramsey graphs, random graphs, rectangles, sample complexity},
	isbn = {0-89791-888-6},
	doi = {10.1145/258533.258611},
	url = {http://doi.acm.org/10.1145/258533.258611},
	author = {Auer,Peter and Long,Philip M. and Srinivasan, Aravind}
}
