Semi-supervised evidential label propagation algorithm for graph data
This addresses community detection with limited supervision for graph data, but it appears incremental.
The paper tackled community detection in graph data by proposing a semi-supervised evidential label propagation algorithm (SELP) that incorporates prior knowledge, resulting in effective detection with outlier identification.
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.