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Exposing multi-relational networks to single-relational network analysis algorithms

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  • Rodriguez, Marko A.
  • Shinavier, Joshua

Abstract

Many, if not most network analysis algorithms have been designed specifically for single-relational networks; that is, networks in which all edges are of the same type. For example, edges may either represent “friendship,” “kinship,” or “collaboration,” but not all of them together. In contrast, a multi-relational network is a network with a heterogeneous set of edge labels which can represent relationships of various types in a single data structure. While multi-relational networks are more expressive in terms of the variety of relationships they can capture, there is a need for a general framework for transferring the many single-relational network analysis algorithms to the multi-relational domain. It is not sufficient to execute a single-relational network analysis algorithm on a multi-relational network by simply ignoring edge labels. This article presents an algebra for mapping multi-relational networks to single-relational networks, thereby exposing them to single-relational network analysis algorithms.

Suggested Citation

  • Rodriguez, Marko A. & Shinavier, Joshua, 2010. "Exposing multi-relational networks to single-relational network analysis algorithms," Journal of Informetrics, Elsevier, vol. 4(1), pages 29-41.
    • Handle: RePEc:eee:infome:v:4:y:2010:i:1:p:29-41
    DOI: 10.1016/j.joi.2009.06.004
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    References listed on IDEAS

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    1. Chen, P. & Xie, H. & Maslov, S. & Redner, S., 2007. "Finding scientific gems with Google’s PageRank algorithm," Journal of Informetrics, Elsevier, vol. 1(1), pages 8-15.
    2. Tim Berners-Lee & James Hendler, 2001. "Publishing on the semantic web," Nature, Nature, vol. 410(6832), pages 1023-1024, April.
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    3. Loe, Chuan Wen & Jensen, Henrik Jeldtoft, 2015. "Comparison of communities detection algorithms for multiplex," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 431(C), pages 29-45.

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