IRIS

Amati, G., Cruciani, A., Pasquini, D., Vocca, P., Angelini, S. (2023). PROPAGATE: A seed propagation framework to compute distance-based metrics on very large graphs. In Machine learning and knowledge discovery in databases: research track (pp.671-688). Cham : Springer Cham [10.1007/978-3-031-43418-1_40].

PROPAGATE: A seed propagation framework to compute distance-based metrics on very large graphs

Amati Giambattista;Cruciani Antonio;Pasquini Daniele;Vocca Paola;
2023-01-01
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023)
Turin, Italy
2023
Rilevanza internazionale
2023
Settore INFO-01/A - Informatica
English
Intervento a convegno
Amati, G., Cruciani, A., Pasquini, D., Vocca, P., Angelini, S. (2023). PROPAGATE: A seed propagation framework to compute distance-based metrics on very large graphs. In Machine learning and knowledge discovery in databases: research track (pp.671-688). Cham : Springer Cham [10.1007/978-3-031-43418-1_40].
Amati, G; Cruciani, A; Pasquini, D; Vocca, P; Angelini, S
02 - Intervento a convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/396803
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