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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-04072022-091103


Tipo di tesi
Tesi di laurea magistrale
Autore
VARESI, MARCO
URN
etd-04072022-091103
Titolo
Self-supervised learning for assortment graph embedding
Dipartimento
INFORMATICA
Corso di studi
INFORMATICA
Relatori
relatore Prof. Bacciu, Davide
correlatore Dott. Trincavelli, Marco
correlatore Dott. Deligiorgis, Georgios
Parole chiave
  • node classification
  • link prediction
  • graph
  • predictive approach
  • self-supervised learning
Data inizio appello
22/04/2022
Consultabilità
Tesi non consultabile
Riassunto
Self-supervised learning on graph structured data has seen rapid growth especially centered on augmentation-based contrastive methods. In this thesis, we will propose a self-supervised predictive approach that aims to reconstruct the information of a node using its neighbors. We demonstrate the effectiveness of our approach in both edge-level and node-level task. Our model shows competitive performance in node classification. In link prediction task, our model outperforms self-supervised model from literature.
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