Improving Graph Neural Networks via Adversarial Robustness Evaluation (2412.10850v1)

Abstract: Graph Neural Networks (GNNs) are currently one of the most powerful types of neural network architectures. Their advantage lies in the ability to leverage both the graph topology, which represents the relationships between samples, and the features of the samples themselves. However, the given graph topology often contains noisy edges, and GNNs are vulnerable to noise in the graph structure. This issue remains unresolved. In this paper, we propose using adversarial robustness evaluation to select a small subset of robust nodes that are less affected by noise. We then only feed the features of these robust nodes, along with the KNN graph constructed from these nodes, into the GNN for classification. Additionally, we compute the centroids for each class. For the remaining non-robust nodes, we assign them to the class whose centroid is closest to them. Experimental results show that this method significantly improves the accuracy of GNNs.

Improving Graph Neural Networks via Adversarial Robustness Evaluation

The paper by Yongyu Wang explores the intrinsic vulnerability of Graph Neural Networks (GNNs) to noisy graph structures and introduces a method to enhance their performance by leveraging adversarial robustness techniques. This work contributes to the ongoing efforts to fortify GNNs against perturbations inherent in graph data, which can significantly degrade model accuracy.

GNNs are a cornerstone algorithm in machine learning, particularly adept at handling graph-structured data by incorporating both node features and graph topology. The prevalent challenge they face is the presence of noisy or irrelevant edges within the graph, which often leads to suboptimal performance. Wang proposes an adversarial robustness evaluation framework that effectively addresses this issue by selecting a subset of robust nodes for GNN training.

Methodological Approach

The proposed method revolves around isolating nodes that demonstrate robustness against adversarial perturbations, leveraging a spectral analysis technique based on the Courant-Fischer theorem. The steps are as follows:

1. Robust Node Selection: Nodes are evaluated for robustness against noisy edges by quantifying distortions between input and output graphs through eigenvalues and eigenvectors. The nodes with the lowest vulnerability scores are deemed robust and selected for further processing.
2. Constructing a Reduced Graph: Using only the robust nodes, a k-nearest neighbors (KNN) graph is constructed. This reduced graph serves as the input for the GNN model, thus maintaining computational efficiency and ensuring noise limitation.
3. Centroid-Based Classification: For nodes not included in the robust set, classification is achieved by assigning each node to the class whose centroid, calculated from the robust nodes, is closest to that node.

The implementation of this approach was tested using the Cora dataset, demonstrating a significant improvement in classification accuracy from 80.40% to 91.06% when the top 40% most robust nodes were used.

Implications and Future Directions

This research holds profound implications for the development of more resilient and efficient graph-based models. By prioritizing robust node features, the dependency on the entire graph with its potential noise is minimized, enhancing prediction reliability. This approach effectively balances computation and accuracy, making it particularly advantageous for large-scale graph datasets.

The future of this work might involve integrating this robustness framework with other advances in GNN architecture and exploring automated methods for optimizing the selection of robust nodes across various types of graphs. Moreover, extending this method to dynamic graphs, where topology changes over time, could further solidify the utility and robustness of GNNs in practical applications.

The focus on adversarial robustness not only elevates the accuracy and stability of GNNs but also sets a precedent for similar models in other domains of machine learning, highlighting a path towards more resilient algorithms in the face of unpredictable data perturbations.