NDLI: Rank Prediction in Graphs with Locally Weighted Polynomial Regression and EM of Polynomial Mixture Models

Content Provider	IEEE Xplore Digital Library
Author	Rallis, M. Vazirgiannis, M.
Copyright Year	2011
Abstract	In this paper we describe a learning framework enabling ranking predictions for graph nodes based solely on individual local historical data. The two learning algorithms capitalize on the multi feature vectors of nodes in graphs that evolve in time. In the first case we use weighted polynomial regression (LWPR) while in the second we consider the Expectation Maximization (EM) algorithm to fit a mixture of polynomial regression models. The first method uses separate weighted polynomial regression models for each web page, while the second algorithm capitalizes on group behavior, thus taking advantage of the possible interdependence between web pages. The prediction quality is quantified as the similarity between the predicted and the actual rankings and compared to alternative baseline predictor. We performed extensive experiments on a real world data set (the Wikipedia graph). The results are very encouraging.
Starting Page	515
Ending Page	519
File Size	192346
Page Count	5
File Format	PDF
ISBN	9781612847580
DOI	10.1109/ASONAM.2011.44
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-25
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Polynomial Regression Mixture Models Clustering Training Web pages Clustering algorithms Expectation-Maximization Maximum Likelihood Estimation Prediction algorithms Polynomials Data models Locally Weighted Regression
Content Type	Text
Resource Type	Article

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