@inproceedings{a3af5b59b4c64ddd977e480e83a890b1,
title = "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.",
keywords = "Belief function theory, Community detection, Label propagation, Semi-supervised learning",
author = "Kuang Zhou and Arnaud Martin and Quan Pan",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 4th International Conference on Belief Functions: Theory and Applications, BELIEF 2016 ; Conference date: 21-09-2016 Through 23-09-2016",
year = "2016",
doi = "10.1007/978-3-319-45559-4_13",
language = "英语",
isbn = "9783319455587",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "123--133",
editor = "Ji{\v r}ina Vejnarov{\'a} and V{\'a}clav Kratochv{\'i}l",
booktitle = "Belief Functions",
}