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arXiv · 2302.01018

Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities

Antonio Longa·Veronica Lachi·Gabriele Santin·Monica Bianchini·Bruno Lepri·Pietro Lio·Franco Scarselli·Andrea Passerini
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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 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.

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Antonio Longa, Veronica Lachi, Gabriele Santin, Monica Bianchini, Bruno Lepri, Pietro Lio, Franco Scarselli, Andrea Passerini. 2023-02-02. Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities. https://arxiv.org/abs/2302.01018

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