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Within-Network Classification Using Local Structure Similarity

  • Christian Desrosiers
  • George Karypis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5781)

Abstract

Within-network classification, where the goal is to classify the nodes of a partly labeled network, is a semi-supervised learning problem that has applications in several important domains like image processing, the classification of documents, and the detection of malicious activities. While most methods for this problem infer the missing labels collectively based on the hypothesis that linked or nearby nodes are likely to have the same labels, there are many types of networks for which this assumption fails, e.g., molecular graphs, trading networks, etc. In this paper, we present a collective classification method, based on relaxation labeling, that classifies entities of a network using their local structure. This method uses a marginalized similarity kernel that compares the local structure of two nodes with random walks in the network. Through experimentation on different datasets, we show our method to be more accurate than several state-of-the-art approaches for this problem.

Keywords

Network semi-supervised learning random walk 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Christian Desrosiers
    • 1
  • George Karypis
    • 1
  1. 1.Department of Computer Science & EngineeringUniversity of MinnesotaTwin Cities

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