Name
Title | ||
|---|---|---|
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds. |
Abstract | ||
|---|---|---|
The space of graphs is often characterized by a nontrivial geometry, which complicates learning and inference in practical applications. A common approach is to use embedding techniques to represent graphs as points in a conventional Euclidean space, but non-Euclidean spaces have often been shown to be better suited for embedding graphs. Among these, constant-curvature Riemannian manifolds (CCMs) ... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TNNLS.2019.2927301 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | DocType | Volume |
Manifolds,Geometry,Extraterrestrial measurements,Topology,Monitoring,Data models | Journal | 31 |
Issue | ISSN | Citations |
6 | 2162-237X | 3 |
PageRank | References | Authors |
0.39 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniele Grattarola | 1 | 8 | 1.95 |
| Daniele Zambon | 2 | 3 | 0.73 |
| Lorenzo Livi | 3 | 304 | 25.67 |
| Cesare Alippi | 4 | 1040 | 115.84 |