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
   246   247   248   249   250   251   252   253   254   255   256