A Machine Learning Approach to Link Prediction for Interlinked Documents
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.
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- 1.Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (1995)Google Scholar
- 2.Hagenbuchner, M., Tsoi, A., Sperduti, A., Kc, M.: Efficient clustering of structured documents using graph self-organizing maps. In: Comparative Evaluation of XML Information Retrieval Systems, pp. 207–221. Springer, Berlin (2008)Google Scholar
- 6.Hagenbuchner, M., Zhang, S., Tsoi, A., Sperduti, A.: Projection of undirected and non-positional graphs using self organizing maps. In: European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, April 22-24 (2009)Google Scholar