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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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    3. Malik Khizar Hayat & Ali Daud, 2017. "Anomaly detection in heterogeneous bibliographic information networks using co-evolution pattern mining," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 149-175, October.

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