Chaotic Neural Oscillators with Deep Graph Neural Network for Node Classification
Pages 168 - 180
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.
References
[1]
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the 4th International Conference on Learning Representations (2016)
[2]
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st Conference on Neural Information Processing Systems, pp. 1024–1034 (2017)
[3]
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: Proceedings of the 5th International Conference on Learning Representations (2017)
[4]
Thekumparampil, K.K., Wang, C., Oh, S., Li, L.J.: Attention based graph neural network for semi-supervised learning (2018). arXiv:1803.03735
[5]
Kipf, T.N., Welling, M.: Variational graph auto-encoders (2016). arXiv:1611.07308
[6]
Veličković, P., Fedus, W., Hamilton, W.L., Liò, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. In: Proceedings of the 6th International Conference on Learning Representations (2018)
[7]
Wang, Y., Hooi, B., Liu, Y., Shah, N.: Graph explicit neural networks: explicitly encoding graphs for efficient and accurate inference. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, February 2023. ACM, Singapore, pp. 348–356 (2023).
[8]
Duong, C.T., Hoang, T.D., Dang, H.T.H., Nguyen, Q.V.H., Aberer, K.: On node features for graph neural networks. arXiv preprint arXiv:1911.08795 (2019)
[9]
Fu, X., King, I.: FedHGN: a federated framework for heterogeneous graph neural networks. In: International Joint Conference on Artificial Intelligence (2023)
[10]
Gori, M., Monfardini, G., Scarselli, F., A new model for learning in graph domains. In: 2005 IEEE International Joint Conference on Neural Networks, 2005. IJCNN’05. Proceedings, vol. 2, pp. 729–734. IEEE (2005)
[11]
Bruna, J., et al.: Spectral networks and locally connected networks on graphs. CoRR abs/1312.6203 (2013). n. pag
[12]
Gilmer, J., et al.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning (2017)
[13]
Aihara K et al. Chaotic neural networks Phys. Lett. A 1990 144 333-340
[14]
Goldwyn, J.H., Shea-Brown, E.T., The what and where of adding channel noise to the Hodgkin-Huxley equations. PLoS Comput. Biol. 7 (2011)
[15]
Wilson HR and Cowan JD Excitatory and inhibitory interactions in localized populations Biophys. J. 1972 12 1-24
[16]
Falcke M, Huerta R, Rabinovich M, et al. Modeling observed chaotic oscillations in bursting neurons: the role of calcium dynamics and IP3 Biol. Cybern. 2000 82 517-527
[17]
Lee RST A transient-chaotic autoassociative network (TCAN) based on Lee oscillators IEEE Trans. Neural Netw. 2004 15 5 1228-1243
[18]
Lee RST Lee-associator—a chaotic auto-associative network for progressive memory recalling Neural Netw. 2006 19 5 644-666
[19]
Shi N, Chen Z, Chen L, and Lee RST CNO-LSTM: a chaotic neural oscillatory long short-term memory model for text classification IEEE Access 2022 10 129564-129579
[20]
Pei, H., et al.: Geom-GCN: geometric graph convolutional networks. In: ICLR (2020)
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Published In
May 2024
405 pages
ISBN:978-981-97-2241-9
DOI:10.1007/978-981-97-2242-6
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 07 May 2024
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