Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships

• URL: http://arxiv.org/abs/2405.03950v1
• Date: Tue, 7 May 2024 02:16:54 GMT
• Title: Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships
• Authors: Qi Zou, Na Yu, Daoliang Zhang, Wei Zhang, Rui Gao,
• Abstract summary: Graph Neural Networks (GNNs) have excelled in learning from graph-structured data. Despite their successes, GNNs are limited by neglecting the context of relationships across graphs. We introduce Relating-Up, a plug-and-play module that enhances GNNs by exploiting inter-graph relationships.
• Score: 17.978546172777342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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