NDLI: Combining Collective Classification and Link Prediction

Content Provider	IEEE Xplore Digital Library
Author	Bilgic, M. Namata, G.M. Getoor, L.
Copyright Year	2007
Description	Author affiliation: Univ. of Maryland, College Park (Bilgic, M.; Namata, G.M.; Getoor, L.)
Abstract	The problems of object classification (labeling the nodes of a graph) and link prediction (predicting the links in a graph) have been largely studied independently. Commonly, object classification is performed assuming a complete set of known links and link prediction is done assuming a fully observed set of node attributes. In most real world domains, however, attributes and links are often missing or incorrect. Object classification is not provided with all the links relevant to correct classification and link prediction is not provided all the labels needed for accurate link prediction. In this paper, we propose an approach that addresses these two problems by interleaving object classification and link prediction in a collective algorithm. We investigate empirically the conditions under which an integrated approach to object classification and link prediction improves performance, and find that performance improves over a wide range of network types, and algorithm settings.
Starting Page	381
Ending Page	386
File Size	163244
Page Count	6
File Format	PDF
ISBN	9780769530192
DOI	10.1109/ICDMW.2007.35
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Conferences Roads Educational institutions Interleaved codes Iterative algorithms Performance analysis Data mining Labeling Information analysis
Content Type	Text
Resource Type	Article

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