Variational autoencoder-based spatio-temporal disentanglement for link prediction in dynamic graph
Abstract Link prediction in dynamic graphs models real-world dynamic networks, providing a concrete and insightful representation of various scenarios. Despite recent advancements in dynamic graph learning, the factorized representations of features across different dimensions and potential causalit...
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| Autori principali: | , , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Springer
2026-01-01
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| Serie: | Complex & Intelligent Systems |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/s40747-025-02217-9 |
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