Original Article

Pattern Analysis and Applications

, Volume 17, Issue 4, pp 739-746

Open Access This content is freely available online to anyone, anywhere at any time.

MapReduce approach to relational influence propagation in complex networks

  • Tomasz KajdanowiczAffiliated withFaculty of Computer Science and Management, Wroclaw University of Technology Email author 
  • , Wojciech IndykAffiliated withFaculty of Computer Science and Management, Wroclaw University of Technology
  • , Przemyslaw KazienkoAffiliated withFaculty of Computer Science and Management, Wroclaw University of Technology

Abstract

The relational label propagation problem for large data sets using MapReduce programming model was considered in the paper. The method we propose estimates class probability in relational domain in the networks. The method was examined on large real telecommunication data set. The results indicated that it could be used successfully to classify networks’ nodes and, thanks to that, new offerings or tariffs might be proposed to customers who belong to other providers. Moreover, basic properties of relational label propagation were examined and reported.

Keywords

MapReduce Relational influence propagation Classification in networks Label propagation Collective classification Relational learning