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

TL;DR

The paper addresses the limitation of traditional GNNs in capturing cross-graph context by introducing Relating-Up, a plug-and-play module consisting of a relation-aware encoder and a feedback training strategy. The relation encoder learns inter-graph relationships via multi-head self-attention over graph embeddings, while the feedback loop jointly refines intra-graph representations using cross-graph insights, formalized with a loss that combines classification, distillation, and alignment terms. Empirically, Relating-Up improves performance across 16 datasets and multiple backbones, and maintains efficient inference by discarding the relation encoder during testing. The work advances graph representation learning by enabling richer, cross-graph reasoning, with practical impact for diverse graph-structured tasks.

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.

Figures (6)

• Figure 1: An illustration of inter-graph relationships. Inter-graph relationships can be similarities or differences in graph properties, structure, or other aspects. Such relationships advancing graph neural networks by capture the dynamics and interactions across graphs.
• Figure 2: Framework of Relating-Up. (a) The overall architecture of Relating-Up. Both graph encoder and relation encoder are jointly optimized from scratch. During the training phase, the relation encoder $f_\mathcal{R}$ dynamically interprets inter-graph relationships to iteratively refines graph encoder $f_\mathcal{G}$ through a feedback training strategy. During the inference phase, the refined graph encoder $f_\mathcal{G}$ is directly deployed for predictions. (b) Information flow in a feedback loop between graph encoder and relational encoder. This cyclical process allows for an evolving understanding of dynamics across graphs, where each iteration contributes to a more profound comprehension of the inter-graph relationships.
• Figure 3: The graph embedding similarity of graph representation of DD dataset extracted from GNN backbone (GIN) and Relating-Up variants. OR: original representation, RAR: relation-aware representation.
• Figure 4: The t-SNE two-dimensional embedding of graph representation of DD dataset extracted from GNN backbone (GIN) and Relating-Up variants. OR: original representation, RAR: relation-aware representation.
• Figure S1: Results of hyperparameter analysis.