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Local feature aggregation algorithm based on graph convolutional network

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References

  1. Lin F, Cohen W W. Semi-supervised classification of network data using very few labels. In: Proceedings of International Conference on Advances in Social Networks Analysis and Mining. 2010, 192–199

  2. Yang Z, Cohen W, Salakhudinov R. Revisiting semi-supervised learning with graph embeddings. In: Proceedings of the 33rd International Conference on Machine Learning. 2016, 40–48

  3. García-Durán A, Bordes A, Usunier N, Grandvalet Y. Combining two and three-way embedding models for link prediction in knowledge bases. Journal of Artificial Intelligence Research, 2016, 55: 715–742

    Article  MathSciNet  MATH  Google Scholar 

  4. Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations. 2016

  5. Hamilton W, Ying Z, Leskovec J. Inductive representation learning on large graphs. In: Proceedings of the 31st Conference on Neural Information Processing Systems. 2017, 1024–1034

  6. Chen J, Zhu J, Song L. Stochastic training of graph convolutional networks with variance reduction. In: Proceedings of the 35th International Conference on Machine Learning. 2018, 942–950

  7. Chen J, Ma T, Xiao C. Fastgcn: fast learning with graph convolutional networks via importance sampling. In: Proceedings of the 6th International Conference on Learning Representations. 2018

  8. Grover A, Leskovec J. Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data mining. 2016, 855–864

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61272209, 61872164), in part by the Program of Science and Technology Development Plan of Jilin Province of China (20190302032GX), and in part by the Fundamental Research Funds for the Central Universities (Jilin University).

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Hao Wang, Liyan Dong & Minghui Sun

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Hao Wang, Liyan Dong & Minghui Sun

Authors
  1. Hao Wang
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  2. Liyan Dong
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  3. Minghui Sun
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Correspondence to Minghui Sun.

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Wang, H., Dong, L. & Sun, M. Local feature aggregation algorithm based on graph convolutional network. Front. Comput. Sci. 16, 163309 (2022). https://doi.org/10.1007/s11704-021-0004-x

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  • DOI: https://doi.org/10.1007/s11704-021-0004-x