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Labeling Actors in Social Networks Using a Heterogeneous Graph Kernel

  • Ngot Bui
  • Vasant Honavar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8393)

Abstract

We consider the problem of labeling actors in social networks where the labels correspond to membership in specific interest groups, or other attributes of the actors. Actors in a social network are linked to not only other actors but also items (e.g., video and photo) which in turn can be linked to other items or actors. Given a social network in which only some of the actors are labeled, our goal is to predict the labels of the remaining actors. We introduce a variant of the random walk graph kernel to deal with the heterogeneous nature of the network (i.e., presence of a large number of node and link types). We show that the resulting heterogeneous graph kernel (HGK) can be used to build accurate classifiers for labeling actors in social networks. Specifically, we describe results of experiments on two real-world data sets that show HGK classifiers often significantly outperform or are competitive with the state-of-the-art methods for labeling actors in social networks.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ngot Bui
    • 1
  • Vasant Honavar
    • 1
  1. 1.College of Information Sciences and TechnologyThe Pennsylvania State UniversityUniversity ParkUSA

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