Loading...
Please wait, while we are loading the content...
Similar Documents
GLEE: Geometric Laplacian Eigenmap Embedding.
| Content Provider | Semantic Scholar |
|---|---|
| Author | Torres, Leo Chan, Kevin S. Eliassi-Rad, Tina |
| Copyright Year | 2019 |
| Abstract | Graph embedding seeks to build a low-dimensional representation of a graph G. This low-dimensional representation is then used for various downstream tasks. One popular approach is Laplacian Eigenmaps, which constructs a graph embedding based on the spectral properties of the Laplacian matrix of G. The intuition behind it, and many other embedding techniques, is that the embedding of a graph must respect node similarity: similar nodes must have embeddings that are close to one another. Here, we dispose of this distance-minimization assumption. Instead, we use the Laplacian matrix to find an embedding with geometric properties instead of spectral ones, by leveraging the so-called simplex geometry of G. We introduce a new approach, Geometric Laplacian Eigenmap Embedding (or GLEE for short), and demonstrate that it outperforms various other techniques (including Laplacian Eigenmaps) in the tasks of graph reconstruction and link prediction. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://leotrs.com/static/GLEE_lanet19.pdf |
| Alternate Webpage(s) | http://leotrs.com/static/GLEE_netsci19.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
