@conference {17861,
	title = {Decentralized, accurate, and low-cost network bandwidth prediction},
	booktitle = {2011 Proceedings IEEE INFOCOM},
	year = {2011},
	month = {2011/04/10/15},
	pages = {6 - 10},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The distributed nature of modern computing makes end-to-end prediction of network bandwidth increasingly important. Our work is inspired by prior work that treats the Internet and bandwidth as an approximate tree metric space. This paper presents a decentralized, accurate, and low cost system that predicts pairwise bandwidth between hosts. We describe an algorithm to construct a distributed tree that embeds bandwidth measurements. The correctness of the algorithm is provable when driven by precise measurements. We then describe three novel heuristics that achieve high accuracy for predicting bandwidth even with imprecise input data. Simulation experiments with a real-world dataset confirm that our approach shows high accuracy with low cost.},
	keywords = {accuracy, approximate tree metric space, Bandwidth, bandwidth allocation, bandwidth measurement, decentralized low cost system, distributed tree, end-to-end prediction, Extraterrestrial measurements, Internet, low-cost network bandwidth prediction, Measurement uncertainty, pairwise bandwidth, Peer to peer computing, Prediction algorithms, trees (mathematics)},
	isbn = {978-1-4244-9919-9},
	doi = {10.1109/INFCOM.2011.5935251},
	author = {Sukhyun Song and Keleher,P. and Bhattacharjee, Bobby and Sussman, Alan}
}
@conference {13362,
	title = {Declarative analysis of noisy information networks},
	booktitle = {2011 IEEE 27th International Conference on Data Engineering Workshops (ICDEW)},
	year = {2011},
	month = {2011/04/11/16},
	pages = {106 - 111},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {There is a growing interest in methods for analyzing data describing networks of all types, including information, biological, physical, and social networks. Typically the data describing these networks is observational, and thus noisy and incomplete; it is often at the wrong level of fidelity and abstraction for meaningful data analysis. This has resulted in a growing body of work on extracting, cleaning, and annotating network data. Unfortunately, much of this work is ad hoc and domain-specific. In this paper, we present the architecture of a data management system that enables efficient, declarative analysis of large-scale information networks. We identify a set of primitives to support the extraction and inference of a network from observational data, and describe a framework that enables a network analyst to easily implement and combine new extraction and analysis techniques, and efficiently apply them to large observation networks. The key insight behind our approach is to decouple, to the extent possible, (a) the operations that require traversing the graph structure (typically the computationally expensive step), from (b) the operations that do the modification and update of the extracted network. We present an analysis language based on Datalog, and show how to use it to cleanly achieve such decoupling. We briefly describe our prototype system that supports these abstractions. We include a preliminary performance evaluation of the system and show that our approach scales well and can efficiently handle a wide spectrum of data cleaning operations on network data.},
	keywords = {Cleaning, Data analysis, data cleaning operations, data management system, data mining, Databases, Datalog, declarative analysis, graph structure, information networks, Noise measurement, noisy information networks, Prediction algorithms, semantics, Syntactics},
	isbn = {978-1-4244-9195-7},
	doi = {10.1109/ICDEW.2011.5767619},
	author = {Moustafa,W. E and Namata,G. and Deshpande, Amol and Getoor, Lise}
}
@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}
}
@article {17667,
	title = {The one-inclusion graph algorithm is near-optimal for the prediction model of learning},
	journal = {IEEE Transactions on Information Theory},
	volume = {47},
	year = {2001},
	month = {2001/03//},
	pages = {1257 - 1261},
	abstract = {Haussler, Littlestone and Warmuth (1994) described a general-purpose algorithm for learning according to the prediction model, and proved an upper bound on the probability that their algorithm makes a mistake in terms of the number of examples seen and the Vapnik-Chervonenkis (VC) dimension of the concept class being learned. We show that their bound is within a factor of 1+o(1) of the best possible such bound for any algorithm},
	keywords = {Computer science, concept class, general-purpose algorithm, graph theory, learning prediction model, learning systems, near-optimal algorithm, Neural networks, one-inclusion graph algorithm, optimisation, Prediction algorithms, prediction theory, Predictive models, probability, Probability distribution, Upper bound, Vapnik-Chervonenkis dimension, Virtual colonoscopy},
	isbn = {0018-9448},
	doi = {10.1109/18.915700},
	author = {Li,Yi and Long,P. M and Srinivasan, Aravind}
}
