Volume 29 Issue 6
Dec.  2020
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WANG Jiahui, GUO Yi, WANG Zhihong, TANG Qifeng, WEN Xinxiu. Advancing Graph Convolution Network with Revised Laplacian Matrix[J]. Chinese Journal of Electronics, 2020, 29(6): 1134-1140. doi: 10.1049/cje.2020.09.015
Citation: WANG Jiahui, GUO Yi, WANG Zhihong, TANG Qifeng, WEN Xinxiu. Advancing Graph Convolution Network with Revised Laplacian Matrix[J]. Chinese Journal of Electronics, 2020, 29(6): 1134-1140. doi: 10.1049/cje.2020.09.015

Advancing Graph Convolution Network with Revised Laplacian Matrix

doi: 10.1049/cje.2020.09.015
Funds:  This work is supported by the National Key Research and Development Program of China (No.2018YFC0807105), the National Natural Science Foundation of China (No.61462073), and the Science and Technology Committee of Shanghai Municipality (No.17DZ1101003, No.18511106602, No.18DZ2252300).
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  • Corresponding author: GUO Yi (corresponding author) received the M.S. degree in computer science from Xidian University, Xi'an, China and the Ph.D. degree in computer science from Heriot-Watt University, Edinburgh, Scotland in 2005. He is currently a Professor at East China University of Science and Technology. His research focus is text mining, information extraction, knowledge discovery and business intelligence analysis. Now he is the member of MIEEE MCMI MIET MBCS and APMGMSP/PRINCE2-Practitioner, and he is also a committee member of National Engineering Laboratory for Big Data Distribution and Exchange Technologies. (Email:guoyi@ecust.edu.cn)
  • Received Date: 2019-10-30
  • Publish Date: 2020-12-25
  • Graph convolution networks are extremely efficient on the graph-structure data, which both consider the graph and feature information. Most existing models mainly focus on redefining the complicated network structure, while ignoring the negative impact of lowquality input data during the aggregation process. This paper utilizes the revised Laplacian matrix to improve the performance of the original model in the preprocessing stage. The comprehensive experimental results testify that our proposed model performs significantly better than other off-the-shelf models with a lower computational complexity, which gains relatively higher accuracy and stability.
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  • S. Guo, Y. Lin, N. Feng, et al., "Attention based spatial-temporal graph convolutional networks for traffic flow forecasting", Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA, pp.25-30, 2019.
    J. Gilmer, S.S. Schoenholz, F.P. Riley, et al., "Neural message passing for quantum chemistry", Proceedings of the 34th International Conference on Machine Learning, Sydney, NSW, Australia, pp.1263-1272, 2017.
    J. Qiu, J. Tang, H. Ma, et al., "Deepinf:Social influence prediction with deep learning", Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, United Kingdom, pp.2110-2119, 2018.
    L. Yao, C. Mao and Y. Luo, "Graph convolutional networks for text classification", Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA, pp.7370-7377, 2019.
    M. Defferrard, X. Bresso and, P. Vandergheynst, "Convolutional neural networks on graphs with fast localized spectral filtering", Advances in Neural Information Processing Systems, Barcelona, Spain, pp.3844-3852, 2016.
    R. Palm, U. Paquet and O. Winther, "Recurrent relational networks", Advances in Neural Information Processing Systems, Montréal, Canada, pp.3368-3378, 2018.
    V. Zambaldi, D. Raposo and A. Santoro, "Relational deep reinforcement learning", https://arxiv.xilesou.top/pdf/1806.01830.pdf, 2018-01-28.
    P. Veličković, G. Cucurull and A. Casanova, "Graph attention networks", International Conference on Learning Representations, Toulon, France, 2017.
    X. Wang, H. Ji, C. Shi, et al., "Heterogeneous graph attention network", The World Wide Web Conference, San Francisco, CA, USA, pp.2022-2032, 2019.
    Z. Ying, J. You, C. Morris, et al., "Hierarchical graph representation learning with differentiable pooling", Advances in Neural Information Processing Systems, Montréal, Canada, pp.4800-4810, 2018.
    H.T. Nguyen and R. Grishman, "Graph convolutional networks with argument-aware pooling for event detection", Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA, pp.7370-7377, 2019.
    J.Chen, T.Ma and C. Xiao, "Fastgcn:Fast learning with graph convolutional networks via importance sampling", International Conference on Learning Representations, Vancouver, BC, Canada, 2018.
    J. Ma, P. Cui and W. Zhu, "Depthlgp:Learning embeddings of out-of-sample nodes in dynamic networks", Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA, pp.370-377, 2018.
    Z. Zhang, P. Cui and W. Zhu, "Deep learning on graphs:A survey", https://arxiv.xilesou.top/pdf/1812.04202.pdf, 2019-11-11.
    X. Dong, D. Thanou, P. Frossard, et al., "Learning Laplacian matrix in smooth graph signal representations", IEEE Transactions on Signal Processing, Vol.64, No.23, pp.6160-6173, 2016.
    W. Hamilton, Z. Ying and J. Leskovec, "Inductive representation learning on large graphs", Advances in Neural Information Processing Systems, Long Beach, CA, USA, pp.1024-1034, 2017.
    J. Atwood and D. Towsley, "Diffusion-convolutional neural networks", Advances in Neural Information Processing Systems, Barcelona, Spain, pp.1993-2001, 2016.
    J. Lee, I. Lee and J. Kang, "Self-attention graph pooling", Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, USA, pp.3734-3743, 2019.
    L.W. Chiang, X. Liu and S. Si, et al., "Cluster-GCN:An efficient algorithm for training deep and large graph convolutional networks", Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Anchorage, AK, USA, pp.257-266, 2019.
    L.R. Murphy, B. Srinivasan and V. Rao, "Relational pooling for graph representations", Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, USA, pp.4663-4673, 2019.
    X. Zhu, Z. Ghahramani and D.J. Lafferty, "Semi-supervised learning using gaussian fields and harmonic functions", Proceedings of the 20th International Conference on Machine Learning, Washington, DC, USA, pp.912-919, 2003.
    B. Perozzi, R. Al-Rfou and S. Skiena, "Deepwalk:Online learning of social representations", Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp.701-710, 2014.
    Q. Lu and L. Getoor, "Link-based classification", Proceedings of the 20th International Conference on Machine Learning, Washington, DC, USA, pp.496-503, 2003.
    Z. Yang, W. W. Cohen and R. Salakhutdinov, "Relational pooling for graph representations", Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA, pp.4663-4673, 2016.
    N.T. Kipf and M. Welling, "Semi-supervised classification with graph convolutional networks", International Conference on Learning Representations, Toulon, France, 2017.
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