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

Published: 19 Aug 2023, Last Modified: 19 Aug 2023Accepted by TMLREveryoneRevisionsBibTeX
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.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: We extend our gratitude to the reviewers for their comments, which have significantly contributed to the refinement and enhancement of our work. In this regard, we are pleased to provide the revised version of the article, wherein each modification has been highlighted in blue. We also addressed the comments of the fourth reviewer and we add the modification required highlighted in red. Thanks again
Assigned Action Editor: ~Shinichi_Nakajima2
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Submission Number: 1129