Dipartimento di Ingegneria informatica, automatica e gestionale

Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data. However, many real-world systems are dynamic in nature, since the graph and node/edge attributes change over time. In recent years, GNN-based models for temporal graphs have emerged as a promising area of research to extend the capabilities of GNNs. In this work, we provide the first comprehensive overview of the current state-of-the-art of temporal GNN, introducing a rigorous formalization of learning settings and tasks and a novel taxonomy categorizing existing approaches in terms of how the temporal aspect is represented and processed. We conclude the survey with a discussion of the most relevant open challenges for the field, from both research and application perspectives.

 

Speaker's short bio: Antonio Longa is an Assistant Professor (RTD-A) at the University of Trento, actively engaged in research within the Structured Machine Learning (SML) Group led by Andrea Passerini. He earned his Ph.D. with honors from the University of Trento and Fondazione Bruno Kessler, under the mentorship of Bruno Lepri. 

 

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ID riunione: 817 8545 2099