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Homophily of Neighborhood in Graph Relational Classifier

  • Peter Vojtek
  • Mária Bieliková
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5901)

Abstract

Quality of collective inference relational graph classifier depends on a degree of homophily in a classified graph. If we increase homophily in the graph, the classifier would assign class-membership to the instances with reduced error rate. We propose to substitute traditionally used graph neighborhood method (based on direct neighborhood of vertex) with local graph ranking algorithm (activation spreading), which provides wider set of neighboring vertices and their weights. We demonstrate that our approach increases homophily in the graph by inferring optimal homophily distribution of a binary Simple Relational Classifier in an unweighted graph. We validate this ability also experimentally using the Social Network of the Slovak Companies dataset.

Keywords

Root Mean Square Error Spreading Activation Direct Neighborhood Neighborhood Method Basic Neighborhood 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Peter Vojtek
    • 1
  • Mária Bieliková
    • 1
  1. 1.Institute of Informatics and Software Engineering Faculty of Informatics and Information TechnologiesSlovak University of TechnologyBratislavaSlovakia

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