Graph Neural Networks With Multi-Level Augmentation for Low-Degree Graph

Abstract

In recent years, Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on graph-structured data, achieving significant success across various fields. Most existing GNNs follow a message passing paradigm that iteratively aggregates features from adjacent nodes to update node representations. However, in low-degree graphs, the limited number of adjacent nodes makes it challenging to fully explore the complex relationships between nodes during the information aggregation process, leading to suboptimal performance in graph learning tasks. Besides, augmentation methods primarily focus on enhancing the richness of node neighborhoods,whichs overlooks the need for GNNs to capture complex relationships during the aggregation process. To this end, this paper introduces a framework for architecture-agnostic data augmentation, termed Multi-level Augmentation (MuLA). The framework consists of three core modules: (1) The graph representation augmentation module enriches the information representation of low-degree nodes by combining their features with those of their unlabeled neighbors; (2) The graph knowledge integration module further enhances node representations by learning the augmented neighborhood information and integrating it with the original features; (3) The Task-Adaptive module dynamically adjusts the framework to accommodate various machine learning tasks. Experimental results demonstrate that the proposed MuLA significantly improves the performance of existing GNNs on semi-supervised node classification and link prediction tasks across multiple datasets, validating its effectiveness and broad applicability.