Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges, and Opportunities
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.
Cite
Text
Longa et al. "Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges, and Opportunities." Transactions on Machine Learning Research, 2023.Markdown
[Longa et al. "Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges, and Opportunities." Transactions on Machine Learning Research, 2023.](https://mlanthology.org/tmlr/2023/longa2023tmlr-graph/)BibTeX
@article{longa2023tmlr-graph,
title = {{Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges, and Opportunities}},
author = {Longa, Antonio and Lachi, Veronica and Santin, Gabriele and Bianchini, Monica and Lepri, Bruno and Lio, Pietro and Scarselli, Franco and Passerini, Andrea},
journal = {Transactions on Machine Learning Research},
year = {2023},
url = {https://mlanthology.org/tmlr/2023/longa2023tmlr-graph/}
}