Semi-Supervised Learning on Directed Graphs
Abstract
Given a directed graph in which some of the nodes are labeled, we inves- tigate the question of how to exploit the link structure of the graph to infer the labels of the remaining unlabeled nodes. To that extent we propose a regularization framework for functions de(cid:2)ned over nodes of a directed graph that forces the classi(cid:2)cation function to change slowly on densely linked subgraphs. A powerful, yet computationally simple classi(cid:2)cation algorithm is derived within the proposed framework. The experimental evaluation on real-world Web classi(cid:2)cation problems demonstrates en- couraging results that validate our approach.
Cite
Text
Zhou et al. "Semi-Supervised Learning on Directed Graphs." Neural Information Processing Systems, 2004.Markdown
[Zhou et al. "Semi-Supervised Learning on Directed Graphs." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/zhou2004neurips-semisupervised/)BibTeX
@inproceedings{zhou2004neurips-semisupervised,
title = {{Semi-Supervised Learning on Directed Graphs}},
author = {Zhou, Dengyong and Hofmann, Thomas and Schölkopf, Bernhard},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1633-1640},
url = {https://mlanthology.org/neurips/2004/zhou2004neurips-semisupervised/}
}