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 stateof-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.

Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges, and Opportunities / Longa, Antonio; Lachi, Veronica; Santin, Gabriele; Bianchini, Monica; Lepri, Bruno; Lio, Pietro; Scarselli, Franco; Passerini, Andrea. - In: TRANSACTIONS ON MACHINE LEARNING RESEARCH. - ISSN 2835-8856. - 2023, 8:(2023), pp. 1-24.

Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges, and Opportunities

Antonio Longa;Bruno Lepri;Andrea Passerini
2023-01-01

Abstract

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 stateof-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.
2023
Longa, Antonio; Lachi, Veronica; Santin, Gabriele; Bianchini, Monica; Lepri, Bruno; Lio, Pietro; Scarselli, Franco; Passerini, Andrea
Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges, and Opportunities / Longa, Antonio; Lachi, Veronica; Santin, Gabriele; Bianchini, Monica; Lepri, Bruno; Lio, Pietro; Scarselli, Franco; Passerini, Andrea. - In: TRANSACTIONS ON MACHINE LEARNING RESEARCH. - ISSN 2835-8856. - 2023, 8:(2023), pp. 1-24.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/400879
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