Tipo di tesi
Tesi di laurea magistrale
Titolo
Self-supervised learning for assortment graph embedding
Corso di studi
INFORMATICA
Parole chiave
- graph
- link prediction
- node classification
- predictive approach
- self-supervised learning
Data inizio appello
22/04/2022
Consultabilità
Tesi non consultabile
Riassunto (Italiano)
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.
