Boosting GNN Performance via Training Sample Selection Based on Adversarial Robustness Evaluation

Graph Neural Networks (GNNs) have established themselves as one of the most powerful neural network architectures, excelling in leveraging graph topology and node features for various tasks. However, GNNs are inherently vulnerable to noise in their inputs. Such noise can significantly degrade their performance. To address this challenge, we propose a novel approach that employs adversarial robustness evaluation techniques to identify nodes in the graph that are most susceptible to noise. By selecting and constructing a training set composed of these particularly noise-prone nodes, we then use them to train a Graph Convolutional Network (GCN). Our experimental results demonstrate that this strategy leads to substantial improvements in the GCN's performance.