Chaotic Neural Oscillators with Deep Graph Neural Network for Node Classification

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.