Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships
CoRR(2024)
摘要
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.
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