A journal of IEEE and CAA , publishes
high-quality papers in English on original
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IEEE/CAA Journal of Automatica Sinica
| Citation: | L. Lin and X. Luo, “Dual channel graph convolutional networks via personalized pagerank,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125492
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M. Azabou, V. Ganesh, S. Thakoor, C. Lin, L. Sathidevi, R. Liu, M. Valko, P. Velickovic, E. Dyer. “Half-Hop: A graph upsampling approach for slowing down message passing”. ICML, 2023.
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F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, K. Weinberger. “Simplifying graph convolutional networks”. ICML, 2019
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C. Deng, Z. Zhao, Y. Wang, Z. Zhang, Z. Feng. “Graphzoom: A multi-level spectral approach for accurate and scalable graph embedding”. ICLR, 2020.
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W. Jin, T. Derr, Y. Wang, Y. Ma, Z. Liu, J. Tang. “Node similarity preserving graph convolutional networks”. WSDM, 2021.
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R. Andersen, F. Chung, K. Lang. “Local graph partitioning using pagerank vectors”. FOCS, 2006.
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L. Lin, R. Li, T. Jia. “Scalable and effective conductance-based graph clustering”. AAAI, 2023.
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Y. Yu, L. Lin, Q. Liu, Z. Wang, X. Ou, T. Jia. “GSD-GNN: generalizable and scalable algorithms for decoupled graph neural networks”. ICMR, 2024.
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T. N. Kipf, M. Welling. “Semi-supervised classification with graph convolutional networks”. ICLR, 2017.
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W. Hamilton, Z. Ying, J. Leskovec. “Inductive representation learning on large graphs”. NIPS, 2017.
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P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, Y. Bengio. “Graph attention networks”. ICLR, 2018.
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W. Zhang, M. Yang, Z. Sheng, Y. Li, W. Ouyang, Y. Tao, B. Cui. “Node dependent local smoothing for scalable graph learning”. NIPS, 2021.
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K. Xu, C. Li, Y. Tian, T. Sonobe, K. Kawarabayashi, S. Jegelka. “Representation learning on graphs with jumping knowledge networks”. ICML, 2018.
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J. Gasteiger, A. Bojchevski, S. Günnemann. “Predict then propagate: Graph neural networks meet personalized pagerank”. ICLR, 2019.
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M. Chen, Z. Wei, B. Ding, Y. Li, Y. Yuan, X. Du, J. Wen. “Scalable graph neural networks via bidirectional propagation”. NIPS, 2021
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K. Ding, Y. Wang, Y. Yang, and H. Liu. “Eliciting structural and semantic global knowledge in unsupervised graph contrastive learning”. AAAI, 2023,
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G. Xue, M. Zhong, T. Qian, and J. Li. 2024. “PSA-GNN: an augmented GNN framework with priori subgraph knowledge”. Neural Networks 173, 106155, 2024.
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W. Zhao, Q. Wu, C. Yang, and J. Yan. “GeoMix: towards geometry-aware data augmentation”. KDD, 2024
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Z. Liao, T. Liu, Y. He, L. Lin. “Effective temporal graph learning via personalized pageRank”. Entropy, 26(7), 2024.
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L. Lin, Y. Yu, Z. Wang, Z. Wang, Y. Zhao, J. Zhao, T. Jia. “PSNE: Efficient spectral sparsification algorithms for scaling network embedding”, CIKM, 2024
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F. Bi, X. Luo, B. Shen, H. Dong, Z. Wang, “Proximal Alternating-Direction-Method-of-Multipliers-Incorporated Nonnegative Latent Factor Analysis”, IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1388−1406, 2023.
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J. Chen, Y. Yuan, X. Luo. Sdgnn: Symmetry-preserving dual-stream graph neural networks. IEEE/CAA J. Autom. Automatica Sinica, vol. 11, no 7, pp, 1717−1719, 2024
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