Node Classification for Signed Social Networks Using Diffuse Interface Methods
Abstract
Signed networks contain both positive and negative kinds of interactions like friendship and enmity. The task of node classification in non-signed graphs has proven to be beneficial in many real world applications, yet extensions to signed networks remain largely unexplored. In this paper we introduce the first analysis of node classification in signed social networks via diffuse interface methods based on the Ginzburg-Landau functional together with different extensions of the graph Laplacian to signed networks. We show that blending the information from both positive and negative interactions leads to performance improvement in real signed social networks, consistently outperforming the current state of the art.
Cite
Text
Mercado et al. "Node Classification for Signed Social Networks Using Diffuse Interface Methods." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019. doi:10.1007/978-3-030-46150-8_31Markdown
[Mercado et al. "Node Classification for Signed Social Networks Using Diffuse Interface Methods." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019.](https://mlanthology.org/ecmlpkdd/2019/mercado2019ecmlpkdd-node/) doi:10.1007/978-3-030-46150-8_31BibTeX
@inproceedings{mercado2019ecmlpkdd-node,
title = {{Node Classification for Signed Social Networks Using Diffuse Interface Methods}},
author = {Mercado, Pedro and Bosch, Jessica and Stoll, Martin},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2019},
pages = {524-540},
doi = {10.1007/978-3-030-46150-8_31},
url = {https://mlanthology.org/ecmlpkdd/2019/mercado2019ecmlpkdd-node/}
}