Semi-supervised Evidential Label Propagation Algorithm for Graph Data

  • Kuang ZhouEmail author
  • Arnaud Martin
  • Quan Pan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9861)


In the task of community detection, there often exists some useful prior information. In this paper, a Semi-supervised clustering approach using a new Evidential Label Propagation strategy (SELP) is proposed to incorporate the domain knowledge into the community detection model. The main advantage of SELP is that it can take limited supervised knowledge to guide the detection process. The prior information of community labels is expressed in the form of mass functions initially. Then a new evidential label propagation rule is adopted to propagate the labels from labeled data to unlabeled ones. The outliers can be identified to be in a special class. The experimental results demonstrate the effectiveness of SELP.


Semi-supervised learning Belief function theory Label propagation Community detection 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Northwestern Polytechnical UniversityXi’anPeople’s Republic of China
  2. 2.DRUID, IRISAUniversity of Rennes 1LannionFrance

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