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Semi-supervised evidential label propagation algorithm for graph data

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-01349851
Contributor : Kuang Zhou <>
Submitted on : Friday, July 29, 2016 - 4:09:57 AM
Last modification on : Friday, March 6, 2020 - 4:10:03 PM
Document(s) archivé(s) le : Sunday, October 30, 2016 - 10:51:18 AM

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SELP_belief2016.pdf
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  • HAL Id : hal-01349851, version 1
  • ARXIV : 1607.08695

Citation

Kuang Zhou, Arnaud Martin, Quan Pan. Semi-supervised evidential label propagation algorithm for graph data. BELIEF 2016 - The 4th International Conference on Belief Functions, Sep 2016, Prague, Czech Republic. ⟨hal-01349851⟩

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