NDLI: On Bayesian interpretation of fact-finding in information networks

Content Provider	IEEE Xplore Digital Library
Author	Dong Wang Abdelzaher, T. Ahmadi, H. Pasternack, J. Roth, D. Gupta, M. Jiawei Han Fatemieh, O. Hieu Le Aggarwal, C.C.
Copyright Year	2011
Description	Author affiliation: IBM Research, Yorktown Heights, N.Y. 10598 (Aggarwal, C.C.) \|\| Department of Computer Science, University of Illinois, Urbana, IL 61801 (Dong Wang; Abdelzaher, T.; Ahmadi, H.; Pasternack, J.; Roth, D.; Gupta, M.; Jiawei Han; Fatemieh, O.; Hieu Le)
Abstract	When information sources are unreliable, information networks have been used in data mining literature to uncover facts from large numbers of complex relations between noisy variables. The approach relies on topology analysis of graphs, where nodes represent pieces of (unreliable) information and links represent abstract relations. Such topology analysis was often empirically shown to be quite powerful in extracting useful conclusions from large amounts of poor-quality information. However, no systematic analysis was proposed for quantifying the accuracy of such conclusions. In this paper, we present, for the first time, a Bayesian interpretation of the basic mechanism used in fact-finding from information networks. This interpretation leads to a direct quantification of the accuracy of conclusions obtained from information network analysis. Hence, we provide a general foundation for using information network analysis not only to heuristically extract likely facts, but also to quantify, in an analytically-founded manner, the probability that each fact or source is correct. Such probability constitutes a measure of quality of information (QoI). Hence, the paper presents a new foundation for QoI analysis in information networks, that is of great value in deriving information from unreliable sources. The framework is applied to a representative fact-finding problem, and is validated by extensive simulation where analysis shows significant improvement over past work and great correspondence with ground truth.
Starting Page	1
Ending Page	8
File Size	476755
Page Count	8
File Format	PDF
ISBN	9781457702679
e-ISBN	9780982443828
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Intl Society of Info Fusion
Subject Keyword	Equations Silicon Bayesian methods Accuracy Mathematical model Data mining Network topology Bayesian inference Information networks sensors
Content Type	Text
Resource Type	Article

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