Name
Abstract | ||
|---|---|---|
Embedding undirected graphs in a Euclidean space has many computational benefits. FastMap is an efficient embedding algorithm that facilitates a geometric interpretation of problems posed on undirected graphs. However, Euclidean distances are inherently symmetric and, thus, Euclidean embeddings cannot be used for directed graphs. In this paper, we present FastMap-D, an efficient generalization of FastMap to directed graphs. FastMap-D embeds vertices using a potential field to capture the asymmetry between the pairwise distances in directed graphs. FastMap-D learns a potential function to define the potential field using a machine learning module. In experiments on various kinds of directed graphs, we demonstrate the advantage of FastMap-D over other approaches. |
Year | Venue | DocType |
|---|---|---|
2020 | SOCS | Conference |
ISSN | Citations | PageRank |
Proceedings of the Twelfth International Symposium on
Combinatorial Search (2020), 48-57 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sriram Gopalakrishnan | 1 | 0 | 0.34 |
| Liron Cohen | 2 | 36 | 11.24 |
| Sven Koenig | 3 | 3125 | 361.22 |
| Kumar T | 4 | 4 | 8.74 |