Improving Node Classification Accuracy in Graph Neural Networks Using PageRank as an Additional Node Feature

Document Type : Original Article

Authors

1 PhD’s Student in Artificial Intelligence and Robotics, Faculty of Artificial Intelligence and Cognitive Sciences, Imam Hossein Comprehensive University, Tehran, Iran

2 Assistant Professor, Faculty of Artificial Intelligence and Cognitive Sciences, Imam Hossein Comprehensive University, Tehran, Iran

Abstract

This paper presents a simple yet effective approach to enhance the performance of Graph Neural Networks (GNNs) in node classification tasks. The proposed method involves incorporating the PageRank score—a global centrality metric—into node feature vectors to integrate broader contextual information beyond local neighborhoods. To evaluate the approach, three well-known GNN architectures—Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and GraphSAGE—are tested on the Cora, CiteSeer, and PubMed citation network datasets. Model performance is assessed using standard metrics such as accuracy, precision, recall, F1 score, along with visual analyses based on Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). Experimental results demonstrate that adding the PageRank score leads to a significant improvement in classification accuracy, particularly in GATs, which benefit more from the additional global information. Despite its simplicity, the proposed method incurs minimal computational overhead and delivers consistent and reliable performance across datasets. Finally, the paper discusses the potential extension of this strategy through the integration of other centrality measures and its application to larger or heterogeneous graphs.

Keywords

Main Subjects

References

[1]     S. Zhang et al., "The combination of a graph neural network technique and brain imaging to diagnose neurological disorders: a review and outlook," Brain Sciences, vol. 13, no. 10, p. 1462, 2023.
[2]     A. Sharma, S. Singh, and S. Ratna, "Graph neural network operators: a review," Multimedia Tools and Applications, vol. 83, no. 8, pp. 23413-23436, 2024.
[3]     D. Klepl, M. Wu, and F. He, "Graph neural network-based eeg classification: A survey," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 493-503, 2024.
[4]     J.-Y. Ryu, E. Elala, and J.-K. K. Rhee, "Quantum graph neural network models for materials search," Materials, vol. 16, no. 12, p. 4300, 2023.
[5]     E. Chien, J. Peng, P. Li, and O. Milenkovic, "Adaptive universal generalized pagerank graph neural network," arXiv preprint arXiv:2006.07988, 2020.
[6]     A. Roth and T. Liebig, "Transforming pagerank into an infinite-depth graph neural network," in Joint European conference on machine learning and knowledge discovery in databases, 2022: Springer, pp. 469-484.
[7]     J. Gasteiger, A. Bojchevski, and S. Günnemann, "Predict then propagate: Graph neural networks meet personalized pagerank," arXiv preprint arXiv:1810.05997, 2018.
[8]     A. Bojchevski et al., "Scaling graph neural networks with approximate pagerank," in Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020, pp. 2464-2473.
[9]     S. Zhang, C. Wang, and J. Zhu, "A Dual Adaptive PageRank Graph Neural Network with Structural Augmentation," in 2024 9th International Conference on Computer and Communication Systems (ICCCS), 2024: IEEE, pp. 1356-1362.
[10] Q. Ma, Z. Fan, C. Wang, and H. Tan, "Graph mixed random network based on pagerank," Symmetry, vol. 14, no. 8, p. 1678, 2022.
[11] J. Choi, "Personalized pagerank graph attention networks," in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022: IEEE, pp. 3578-3582.
[12] S. Zhang et al., "A survey on graph neural network acceleration: Algorithms, systems, and customized hardware," arXiv preprint arXiv:2306.14052, 2023.
 
Applied and basic Machine intelligence research
Volume 3, Issue 1
August 2025
Pages 127-144

Files

Share

How to cite

Statistics