ROLAND: Graph Learning Framework for Dynamic Graphs

@article{You2022ROLANDGL,
  title={ROLAND: Graph Learning Framework for Dynamic Graphs},
  author={Jiaxuan You and Tianyu Du and Jure Leskovec},
  journal={Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year={2022}
}
  • Jiaxuan YouTianyu DuJ. Leskovec
  • Published 14 August 2022
  • Computer Science
  • Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Graph Neural Networks (GNNs) have been successfully applied to many real-world static graphs. However, the success of static graphs has not fully translated to dynamic graphs due to the limitations in model design, evaluation settings, and training strategies. Concretely, existing dynamic GNNs do not incorporate state-of-the-art designs from static GNNs, which limits their performance. Current evaluation settings for dynamic GNNs do not fully reflect the evolving nature of dynamic graphs… 

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