Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships
<|reference_start|>Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships: Graph Neural Networks (GNNs) have excelled in learning from graph-structured data, especially in understanding the relationships within a single graph, i.e., intra-graph relationships. Despite their successes, GNNs are limited by neglecting the context of relationships across graphs, i.e., inter-graph relationships. Recognizing the potential to extend this capability, we introduce Relating-Up, a plug-and-play module that enhances GNNs by exploiting inter-graph relationships. This module incorporates a relation-aware encoder and a feedback training strategy. The former enables GNNs to capture relationships across graphs, enriching relation-aware graph representation through collective context. The latter utilizes a feedback loop mechanism for the recursively refinement of these representations, leveraging insights from refining inter-graph dynamics to conduct feedback loop. The synergy between these two innovations results in a robust and versatile module. Relating-Up enhances the expressiveness of GNNs, enabling them to encapsulate a wider spectrum of graph relationships with greater precision. Our evaluations across 16 benchmark datasets demonstrate that integrating Relating-Up into GNN architectures substantially improves performance, positioning Relating-Up as a formidable choice for a broad spectrum of graph representation learning tasks.<|reference_end|>
@article{zou2024relating-up:,
    title={Relating-Up: Advancing Graph Neural Networks through Inter-Graph
  Relationships},
    author={Qi Zou, Na Yu, Daoliang Zhang, Wei Zhang, Rui Gao},
    journal={arXiv preprint arXiv:2405.03950},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03950},
    primaryClass={cs.LG cs.AI}
}