Abstract
Node classification is a pivotal task in spam detection, community identification, and social network analysis. Compared with traditional graph learning methods, Graph Neural Networks (GNN) show superior performance in prediction tasks, but essentially rely on the characteristics of adjacent nodes. This paper proposed a novel Chaotic Neural Oscillator Feature Selection Graph Neural Network (CNO_FSGNN) model integrating Lee Oscillator which serves as a chaotic memory association to enhance the processing of transient information and transitions between distinct behavioral patterns and synchronization of relevant networks, and a Feature Selection Graph Neural Network to address the limitations. Consequently, the synthesis can improve mean classification accuracy across six homogeneous and heterogeneous datasets notably in Squirrel dataset, and can mitigate over-smoothing concerns in deep layers reducing model execution time.
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Acknowledgements
This paper was supported in part by the Guangdong Provincial Key Laboratory IRADS (2022B1212010006, R0400001-22), and Guangdong Province F1 project grant UICR0400050-21CTL.
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Zhang, L., Lee, R.S.T. (2024). Chaotic Neural Oscillators with Deep Graph Neural Network for Node Classification. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14645. Springer, Singapore. https://doi.org/10.1007/978-981-97-2242-6_14
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DOI: https://doi.org/10.1007/978-981-97-2242-6_14
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