Semi-supervised evidential label propagation algorithm for graph data

TL;DR

This paper introduces SELP, a semi-supervised community detection algorithm that incorporates prior knowledge through evidential label propagation, effectively guiding the detection process and identifying outliers.

Contribution

It presents a novel evidential label propagation strategy that leverages limited supervised information for improved community detection in graph data.

Findings

SELP effectively incorporates prior knowledge into community detection.

The method can identify outliers as a separate class.

Experimental results demonstrate SELP's superior performance.

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.