| Citation: | T. He, Z. Duan, and X. Luo, “Modularized graph convolutional network,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2025.125336 |
| [1] |
M. E. Newman, “Modularity and community structure in networks,” Proc. Natl. Acad. Sci. USA, vol. 103, no. 23, pp. 8577–8582, 2006. doi: 10.1073/pnas.0601602103
|
| [2] |
X. Hong, T. Zhang, Z. Cui, and J. Yang, “Variational gridded graph convolution network for node classification,” IEEE/CAA J. Autom. Sinica., vol. 8, no. 10, pp. 1697–1708, 2021. doi: 10.1109/JAS.2021.1004201
|
| [3] |
Q. Zhu, Q. Xiong, Z. Yang, and Y. Yu, “RGCNU: Recurrent graph convolutional network with uncertainty estimation for remaining useful life prediction,” IEEE/CAA J. Autom. Sinica., vol. 10, no. 7, pp. 1640–1642, 2023. doi: 10.1109/JAS.2023.123369
|
| [4] |
F. Bi, T. He, and X. Luo, “A two-stream light graph convolution network-based latent factor model for accurate cloud service QOS estimation,” in Proc. ICDM, 2022, pp. 855–860.
|
| [5] |
T. He, Y. Liu, Y. S. Ong, X. Wu, and X. Luo, “Polarized message-passing in graph neural networks,” Artificial Intelligence, vol. 331, p. 104129, 2024. doi: 10.1016/j.artint.2024.104129
|
| [6] |
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” NeurIPS, vol. 30, 2017.
|
| [7] |
P. Veličković, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio, and R. D. Hjelm, “Deep graph infomax,” in proc. ICLR, 2019.
|
| [8] |
F. Wu, A. Souza, T. Zhang, C. Proc., T. Yu, and K. Weinberger, “Simplifying graph convolutional networks,” in ICML, 2019, pp. 6861–6871.
|
| [9] |
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proc. ICLR, 2016.
|
| [10] |
J. Gasteiger, A. Bojchevski, and S. Günnemann, “Predict then propagate: Graph neural networks meet personalized pagerank,” in Proc. ICLR, 2019.
|
| [11] |
K. Xu, C. Li, Y. Tian, T. Sonobe, K.-i. Kawarabayashi, and S. Jegelka, “Representation learning on graphs with jumping knowledge networks,” in Proc. ICML, 2018, pp. 5453–5462.
|
| [12] |
Y. Ma, X. Liu, T. Zhao, Y. Liu, J. Tang, and N. Shah, “A unified view on graph neural networks as graph signal denoising,” in Proc. ACM Int. Conf. Inf. Knowl. Manag., 2021, pp. 1202–1211.
|
| [13] |
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in Proc. ICLR, 2018.
|
| [14] |
T. He, Y. S. Ong, and L. Bai, “Learning conjoint attentions for graph neural nets,” NeurIPS, vol. 34, pp. 2641–2653, 2021.
|
| [15] |
H. Zhou, T. He, Y. S. Ong, G. Cong, and Q. Chen, “Differentiable clustering for graph attention,” IEEE Trans. Knowl. Data Eng., 2024.
|
| [16] |
A. Clauset, M. E. Newman, and C. Moore, “Finding community structure in very large networks,” Phys. Rev. E, vol. 70, no. 6, p. 66111, 2004. doi: 10.1103/PhysRevE.70.066111
|
| [17] |
M. McPherson, L. Smith-Lovin, and J. M. Cook, “Birds of a feather: Homophily in social networks,” Annu. Rev. Sociol., vol. 27, no. 1, pp. 415–444, 2001. doi: 10.1146/annurev.soc.27.1.415
|
| [18] |
K. Huang, Y. G. Wang, M. Li, and P. Lio, “How universal polynomial bases enhance spectral graph neural networks: Heterophily, over-smoothing, and over-squashing,” in Proc. ICML, 2024.
|
| [19] |
C. Huang, M. Li, F. Cao, H. Fujita, Z. Li, and X. Wu, “Are graph convolutional networks with random weights feasible?” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 3, pp. 2751–2768, 2023. doi: 10.1109/TPAMI.2022.3183143
|
| [20] |
M. Chen, Z. Wei, Z. Huang, B. Ding, and Y. Li, “Simple and deep graph convolutional networks,” in Proc. ICML, 2020, pp. 1725–1735.
|