Cargando…
Time-varying graph representation learning via higher-order skip-gram with negative sampling
Representation learning models for graphs are a successful family of techniques that project nodes into feature spaces that can be exploited by other machine learning algorithms. Since many real-world networks are inherently dynamic, with interactions among nodes changing over time, these techniques...
| Autores principales: | , |
|---|---|
| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer Berlin Heidelberg
2022
|
| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143726/ https://www.ncbi.nlm.nih.gov/pubmed/35668814 http://dx.doi.org/10.1140/epjds/s13688-022-00344-8 |