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A machine learning approach to link prediction for interlinked documents
Abstract This paper provides an explanation to how This paper provides an explanation to how a recently developed machine learning approach, namely the Probability Measure Graph Self-Organizing Map (PM-GraphSOM) can be used for the generation of links between referenced or otherwise interlinked documents. This new generation of SOM models are capable of projecting generic graph structured data onto a fixed sized display space. Such a mechanism is normally used for dimension reduction, visualization, or clustering purposes. This paper shows that the PM-GraphSOM training algorithm "inadvertently" encodes relations that exist between the atomic elements in a graph. If the nodes in the graph represent documents, and the links in the graph represent the reference (or hyperlink) structure of the documents, then it is possible to obtain a set of links for a test document whose link structure is unknown. A significant finding of this paper is that the described approach is scalable in that links can be extracted in linear time. It will also be shown that the proposed approach is capable of predicting the pages which would be linked to a new document, and is capable of predicting the links to other documents from a given test document. The approach is applied to web pages from Wikipedia, a relatively large XML text database consisting of many referenced documents.e consisting of many referenced documents.
Abstractsub This paper provides an explanation to how This paper provides an explanation to how a recently developed machine learning approach, namely the Probability Measure Graph Self-Organizing Map (PM-GraphSOM) can be used for the generation of links between referenced or otherwise interlinked documents. This new generation of SOM models are capable of projecting generic graph structured data onto a fixed sized display space. Such a mechanism is normally used for dimension reduction, visualization, or clustering purposes. This paper shows that the PM-GraphSOM training algorithm "inadvertently" encodes relations that exist between the atomic elements in a graph. If the nodes in the graph represent documents, and the links in the graph represent the reference (or hyperlink) structure of the documents, then it is possible to obtain a set of links for a test document whose link structure is unknown. A significant finding of this paper is that the described approach is scalable in that links can be extracted in linear time. It will also be shown that the proposed approach is capable of predicting the pages which would be linked to a new document, and is capable of predicting the links to other documents from a given test document. The approach is applied to web pages from Wikipedia, a relatively large XML text database consisting of many referenced documents.e consisting of many referenced documents.
Bibtextype inproceedings  +
Doi 10.1007/978-3-642-14556-8_34  +
Has author Kc M. + , Chau R. + , Hagenbuchner M. + , Tsoi A.C. + , Lee V. +
Has extra keyword Dimension reduction + , Graph structured data + , Hyperlinks + , Linear time + , Link prediction + , Link structure + , Machine learning + , Probability measures + , Text database + , Training algorithms + , Web page + , Wikipedia + , Conformal mapping + , Forecasting + , Hypertext systems + , Learning algorithms + , Learning systems + , Markup languages + , Visualisation + , Websites + , XML + , Security of data +
Isbn 3642145558; 9783642145551  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 342–354  +
Published in Lecture Notes in Computer Science +
Title A machine learning approach to link prediction for interlinked documents +
Type conference paper  +
Volume 6203 LNCS  +
Year 2010 +
Creation dateThis property is a special property in this wiki. 6 November 2014 14:40:54  +
Categories Publications without keywords parameter  + , Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 14:40:54  +
DateThis property is a special property in this wiki. 2010  +
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