Saliency-Aware Regularized Graph Neural Network (2401.00755v1)

Abstract: The crux of graph classification lies in the effective representation learning for the entire graph. Typical graph neural networks focus on modeling the local dependencies when aggregating features of neighboring nodes, and obtain the representation for the entire graph by aggregating node features. Such methods have two potential limitations: 1) the global node saliency w.r.t. graph classification is not explicitly modeled, which is crucial since different nodes may have different semantic relevance to graph classification; 2) the graph representation directly aggregated from node features may have limited effectiveness to reflect graph-level information. In this work, we propose the Saliency-Aware Regularized Graph Neural Network (SAR-GNN) for graph classification, which consists of two core modules: 1) a traditional graph neural network serving as the backbone for learning node features and 2) the Graph Neural Memory designed to distill a compact graph representation from node features of the backbone. We first estimate the global node saliency by measuring the semantic similarity between the compact graph representation and node features. Then the learned saliency distribution is leveraged to regularize the neighborhood aggregation of the backbone, which facilitates the message passing of features for salient nodes and suppresses the less relevant nodes. Thus, our model can learn more effective graph representation. We demonstrate the merits of SAR-GNN by extensive experiments on seven datasets across various types of graph data. Code will be released.

• The paper introduces SAR-GNN, a framework that integrates node-level feature learning with global graph memory to highlight the importance of salient nodes.
• The model achieves significant improvements in classification accuracy and robustness across seven diverse datasets compared to traditional GNNs.
• SAR-GNN modulates feature aggregation by up-weighting globally important nodes, offering actionable insights for future neural network designs.

Introduction to SAR-GNN

Understanding the structure of graphical data is essential in various applications, from social networks analysis to chemistry and bioinformatics. Graph Neural Networks (GNNs) are powerful tools that have been developed for these tasks, especially in graph classification. However, many GNNs primarily focus on local feature aggregation which often falls short in capturing the global structure necessary for graph classification.

The SAR-GNN Framework

To overcome limitations of existing GNNs, a novel framework known as Saliency-Aware Regularized Graph Neural Network (SAR-GNN) has been introduced. The framework addresses two significant challenges: recognizing the global importance or 'saliency' of nodes for classification tasks and devising an effective global graph representation.

SAR-GNN comprises two pivotal components: a conventional GNN that serves as a backbone for node-level feature learning and the Graph Neural Memory responsible for generating a distilled representation of the graph from node features. The integration of these components allows SAR-GNN to assign varying significance to different nodes based on their global saliency. This is achieved by measuring the semantic similarity between node features and the graph representation.

Using the learned saliency distribution, SAR-GNN modulates the aggregation mechanism—enhancing the feature passing from salient nodes while diminishing the influence of less relevant ones.

Experimental Validation

To verify the effectiveness of SAR-GNN, researchers conducted rigorous testing on seven diverse datasets encompassing various graph types. Experimentation showed significant improvements over traditional GNNs. The SAR-GNN not only raised the bar for classification accuracy but also proved to be more robust and adaptable when built upon different base models of GNNs.

Conclusion and Contributions

The SAR-GNN sets a new standard in graph classification by improving how saliency is handled and by generating more powerful graph representations. Its robust experimental performance suggests a promising path for GNN development, tailoring the message-passing mechanism to emphasize features from globally salient nodes.

Ultimately, the approach may influence future designs of neural networks for graphical data, opening doors to more nuanced and insightful analyses across various domains.