Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
This survey addresses the need for structured understanding in extending GNNs to dynamic systems, benefiting researchers and practitioners in graph-based machine learning.
The paper provides a comprehensive survey of Graph Neural Networks (GNNs) for temporal graphs, formalizing learning settings and proposing a taxonomy to categorize existing approaches based on temporal representation and processing.
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.