SCOUT: Structure-Aware Aspect and Anchor-Count Selection for Node Attribute Augmentation via Positional Information

Abstract

In the absence of node attributes, Graph Neural Networks (GNNs) often fail to distinguish locally isomorphic nodes, leading to suboptimal performance. To compensate for this, Positional Information (PI) augmentation has emerged as a powerful technique, which generates attributes by selecting representative nodes as anchors and encoding node-to-anchor distances to other nodes. However, the performance of PI-based methods hinges on two graph-dependent choices: 1) the structural measures used for anchor selection and distance metrics, and 2) the anchor-count K. To obviate manual selections, we propose SCOUT, a model-agnostic augmentation framework that learns a graph-level selector to identify the optimal structural measure and adaptively determines the anchor-count K tailored to each graph and task. Subsequently, leveraging the heavy-tailed distribution typically observed in node centrality, SCOUT utilizes an elbow detection method on the ranked centrality curve to adaptively determine the K most representative nodes as anchors. SCOUT is model-agnostic and enhances various GNNs across downstream tasks. It achieves an improvement of 26.88% in Hits@20 for link prediction on ogbl-ddi and 4.52% accuracy points for node classification on ogbn-arxiv without original attributes; with original attributes, it also brings additional gains of 6.15% AUC on Cora and 11.69% accuracy points on ogbn-arxiv. The source code of SCOUT is available at https://github.com/seinkim01/SCOUT.git.