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A Data-centric graph neural network for node classification of heterophilic networks | International Journal of Machine Learning and Cybernetics Skip to main content
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A Data-centric graph neural network for node classification of heterophilic networks

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Abstract

In the real world, numerous heterophilic networks effectively model the tendency of similar entities to repel each other and dissimilar entities to be attracted to each other within complex systems. Concerning the node classification problem in heterophilic networks, a plethora of heterophilic Graph Neural Networks (GNNs) have emerged. However, these GNNs demand extensive hyperparameter tuning, activation function selection, parameter initialization, and other configuration settings, particularly when dealing with diverse heterophilic networks and resource constraints. This situation raises a fundamental question: Can a method be designed to directly preprocess heterophilic networks and then leverage the trained models in network representation learning systems? In this paper, we propose a novel approach to transform heterophilic network structures. Specifically, we train an edge classifier and subsequently employ this edge classifier to transform a heterophilic network into its corresponding homophilic counterpart. Finally, we conduct experiments on heterophilic network datasets with variable sizes, demonstrating the effectiveness of our approach. The code and datasets are publicly available at https://github.com/xueyanfeng/D_c_GNNs.

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Data availibility

Data supporting this study are openly available at https://github.com/xueyanfeng/D_c_GNNs.

Notes

  1. https://docs.dgl.ai/tutorials/blitz/index.html.

  2. https://pytorch-geometric.readthedocs.io/en/latest/.

  3. https://docs.cogdl.ai/en/latest/.

  4. https://graph-learn.readthedocs.io/en/latest/index_en.html.

  5. https://github.com/tencent/plato.

  6. https://pgl.readthedocs.io/en/latest/.

  7. https://github.com/facebookresearch/PyTorch-BigGraph.

  8. Scikit-learn is an open-source machine learning library that supports both supervised and unsupervised learning techniques.

  9. https://github.com/ChandlerBang/SimP-GCN.

  10. https://github.com/bdy9527/FAGCN.

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Acknowledgements

This research is supported in part by National Natural Science Foundation of China under Grant 12231012, Research of Technological Important Programs in the city of Lüliang, China (No. 2022GXYF18), Innovation Project of Higher Education Teaching Reform in Shanxi (No. J20221164, 2022YJJG310) and Natural Science Foundation in Shanxi (No. 202203021221229).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Lyuliang University, Lüliang, China

    Yanfeng Xue & Wenlian Gao

  2. Complex Systems Research Center, Shanxi University, Taiyuan, China

    Zhen Jin

  3. Key Laboratory of Complex Systems and Data Science of Ministry of Education, Shanxi University, Taiyuan, China

    Zhen Jin

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  1. Yanfeng Xue
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  2. Zhen Jin
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  3. Wenlian Gao
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Correspondence to Zhen Jin.

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Xue, Y., Jin, Z. & Gao, W. A Data-centric graph neural network for node classification of heterophilic networks. Int. J. Mach. Learn. & Cyber. 15, 3413–3423 (2024). https://doi.org/10.1007/s13042-024-02100-y

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