Directed Graph Embedding: An Algorithm Based on Continuous Limits of Laplacian-Type Operators
Abstract
This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information. We model the observed graph as a sample from a manifold endowed with a vector field, and we design an algo- rithm that separates and recovers the features of this process: the geometry of the manifold, the data density and the vector field. The algorithm is motivated by our analysis of Laplacian-type operators and their continuous limit as generators of diffusions on a manifold. We illustrate the recovery algorithm on both artificially constructed and real data.
Cite
Text
Perrault-joncas and Meila. "Directed Graph Embedding: An Algorithm Based on Continuous Limits of Laplacian-Type Operators." Neural Information Processing Systems, 2011.Markdown
[Perrault-joncas and Meila. "Directed Graph Embedding: An Algorithm Based on Continuous Limits of Laplacian-Type Operators." Neural Information Processing Systems, 2011.](https://mlanthology.org/neurips/2011/perraultjoncas2011neurips-directed/)BibTeX
@inproceedings{perraultjoncas2011neurips-directed,
title = {{Directed Graph Embedding: An Algorithm Based on Continuous Limits of Laplacian-Type Operators}},
author = {Perrault-joncas, Dominique C. and Meila, Marina},
booktitle = {Neural Information Processing Systems},
year = {2011},
pages = {990-998},
url = {https://mlanthology.org/neurips/2011/perraultjoncas2011neurips-directed/}
}