Page 251 - 《软件学报》2026年第2期
P. 251
730 软件学报 2026 年第 37 卷第 2 期
network approach. In: Proc. of the 34th AAAI Conf. on Artificial Intelligence. New York: ACM, 2020. 27–34. [doi: 10.1609/aaai.v34i01.
5330]
[20] Rao X, Chen LS, Liu Y, Shang S, Yao B, Han P. Graph-flashback network for next location recommendation. In: Proc. of the 28th ACM
SIGKDD Conf. on Knowledge Discovery and Data Mining. Washington: ACM, 2022. 1463–1471. [doi: 10.1145/3534678.3539383]
[21] Zhang WF, Mao JW, Cao Y, Xu CF. Multiplex graph neural networks for multi-behavior recommendation. In: Proc. of the 29th ACM Int’l
Conf. on Information & Knowledge Management. ACM, 2020. 2313–2316. [doi: 10.1145/3340531.3412119]
[22] Fu CF, Zheng GJ, Huang C, Yu YW, Dong JY. Multiplex heterogeneous graph neural network with behavior pattern modeling. In: Proc.
of the 29th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining. Long Beach: ACM, 2023. 482–494. [doi: 10.1145/3580305.
3599441]
[23] Yu PY, Fu CF, Yu YW, Huang C, Zhao ZY, Dong JY. Multiplex heterogeneous graph convolutional network. In: Proc. of the 28th ACM
SIGKDD Conf. on Knowledge Discovery and Data Mining. Washington: ACM, 2022. 2377–2387. [doi: 10.1145/3534678.3539482]
[24] Liu YX, Jin M, Pan SR, Zhou C, Zheng Y, Xia F, Yu P. Graph self-supervised learning: A survey. IEEE Trans. on Knowledge and Data
Engineering, 2023, 35(6): 5879–5900. [doi: 10.1109/TKDE.2022.3172903]
[25] Perozzi B, Al-Rfou R, Skiena S. DeepWalk: Online learning of social representations. In: Proc. of the 20th ACM SIGKDD Int’l Conf. on
Knowledge Discovery and Data Mining. New York: ACM, 2014. 701–710. [doi: 10.1145/2623330.2623732]
[26] Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. In: Proc. of the 5th Int’l Conf. on Learning
Representations. 2017. 1–14.
[27] Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y. Graph attention networks. In: Proc. of the 6th Int’l Conf. on
Learning Representations. 2018. 1–12.
[28] Wu YJ, Lian DF, Xu YH, Wu L, Chen EH. Graph convolutional networks with Markov random field reasoning for social spammer
detection. In: Proc. of the 34th AAAI Conf. on Artificial Intelligence. New York: ACM, 2020. 1054–1061. [doi: 10.1609/aaai.v34i01.
5455]
[29] Wang X, Zhu MQ, Bo DY, Cui P, Shi C, Pei J. AM-GCN: Adaptive multi-channel graph convolutional networks. In: Proc. of the 26th
ACM SIGKDD Int’l Conf. on Knowledge Discovery & Data Mining. ACM, 2020. 1243–1253. [doi: 10.1145/3394486.3403177]
[30] Yun S, Jeong M, Kim R, Kang J, Kim HJ. Graph Transformer networks. In: Proc. of the 33rd Int’l Conf. on Neural Information
Processing Systems. Vancouver: Curran Associates Inc., 2019. 1073.
[31] Zhang HM, Qiu LW, Yi LL, Song YQ. Scalable multiplex network embedding. In: Proc. of the 27th Int’l Joint Conf. on Artificial
Intelligence. Stockholm: AAAI Press, 2018. 3082–3088.
[32] Fu XY, Zhang JN, Meng ZQ, King I. MAGNN: Metapath aggregated graph neural network for heterogeneous graph embedding. In: Proc.
of the 2020 Web Conf. Taipei: ACM, 2020. 2331–2341. [doi: 10.1145/3366423.3380297]
[33] Hu ZN, Dong YX, Wang KS, Sun YZ. Heterogeneous graph Transformer. In: Proc. of the 2020 Web Conf. Taipei: ACM, 2020.
2704–2710. [doi: 10.1145/3366423.3380027]
[34] Duan HR, Xie C, Li LY. Reserving-masking-reconstruction model for self-supervised heterogeneous graph representation. In: Proc. of the
30th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining. Barcelona: ACM, 2024. 689–700. [doi: 10.1145/3637528.
3671719]
[35] Das SS, Ferdous SM, Halappanavar MM, Serra E, Pothen A. AGS-GNN: Attribute-guided sampling for graph neural networks. In: Proc.
of the 30th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining. Barcelona: ACM, 2024. 538–549. [doi: 10.1145/3637528.
3671940]
[36] Yu LF, Shen JJ, Li JY, Lerer A. Scalable graph neural networks for heterogeneous graphs. arXiv:2011.09679, 2020.
[37] Cen YK, Zou X, Zhang JW, Yang HX, Zhou JR, Tang J. Representation learning for attributed multiplex heterogeneous network. In:
Proc. of the 25th ACM SIGKDD Int’l Conf. on Knowledge Discovery & Data Mining. Anchorage: ACM, 2019. 1358–1368. [doi: 10.
1145/3292500.3330964]
[38] Wang X, Ji HY, Shi C, Wang B, Ye YF, Cui P, Yu PS. Heterogeneous graph attention network. In: Proc. of the 2019 World Wide Web
Conf. San Francisco: ACM, 2019. 2022–2032. [doi: 10.1145/3308558.3313562]
[39] Peng Z, Huang WB, Luo MN, Zheng QH, Rong Y, Xu TY, Huang JZ. Graph representation learning via graphical mutual information
maximization. In: Proc. of the 2020 Web Conf. Taipei: ACM, 2020. 259–270. [doi: 10.1145/3366423.3380112]
[40] Hang JQ, Hong ZQ, Feng XY, Wang G, Yang G, Li F, Song XN, Zhang DS. Paths2Pair: Meta-path based link prediction in billion-scale
commercial heterogeneous graphs. In: Proc. of the 30th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining. Barcelona:
ACM, 2024. 5082–5092. [doi: 10.1145/3637528.3671563]
[41] Hassani K, Khasahmadi AH. Contrastive multi-view representation learning on graphs. In: Proc. of the 37th Int’l Conf. on Machine

