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

arXiv:2405.03950v1 Announce Type: new
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 …

abstract arxiv capability context cs.ai cs.lg data gnns graph graph neural networks graphs networks neural networks relationships structured data through type understanding