NFT @ 06032026: On the Alignment of Self-Supervised Graph Representation Learning Methods

Описание: #researchatnecstlab - #NECSTFridayTalk

On Friday, March 6, 2026, we had a new talk for the series #NECSTFridayTalk.

During this talk, we had, as speaker, Leonardo De Grandis, PhD at Dipartimento di Elettronica, Informazione e Bioingegneria.

In the following, you can find the details about the talk:
📌 Title: On the Alignment of Self-Supervised Graph Representation Learning Methods

📌 Abstract: Graph Neural Networks are well suited for modeling heterogeneous knowledge from many domains, such as social networks, biology, and chemistry. However, the necessity of labels is often a challenge for their training, therefore self-supervised techniques represent a promising approach to learn powerful, generalizable, and transferable representations. This talk presents Graph-DINO, an adaptation of the well known DINO framework from computer vision, for self-supervised representation learning on graph-based data. The learned embeddings are also evaluated from an alignment perspective, leveraging complementary local and global alignment metrics, to examine its behavior with respect to other unsupervised methods.

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