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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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.