Page 266 - 《软件学报》2026年第7期
P. 266

岳立楠 等: 面向图分类任务的互补感知证据提取方法                                                       2951


                 [36]   Chen YQ, Bian YT, Han B, Cheng J. How interpretable are interpretable graph neural networks? In: Proc. of the 41st Int’l Conf. on
                     Machine Learning. PMLR, 2024. 6413–6456.
                 [37]   Ribeiro MT, Singh S, Guestrin C. “Why should I trust you?”: Explaining the predictions of any classifier. In: Proc. of the 22nd ACM
                     SIGKDD  Int’l  Conf.  on  Knowledge  Discovery  and  Data  Mining.  San  Francisco:  ACM,  2016.  1135–1144.  [doi:  10.1145/2939672.
                     2939778]
                 [38]   Li P, Song SH, Zhang Y, Cao HW, Ye XC, Tang ZM. HSEGRL: A hierarchical self-explainable graph representation learning model.
                     Journal of Computer Research and Development, 2024, 61(8): 1993–2007 (in Chinese with English abstract). [doi: 10.7544/issn1000-
                     1239.202440142]
                 [39]   Wang YY, Zhang M, Yang JR, Xu SK, Chen YX. Research on fairness in deep learning models. Ruan Jian Xue Bao/Journal of Software,
                     2023, 34(9): 4037–4055 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6872.htm [doi: 10.13328/j.cnki.jos.006872]
                 [40]   Yue LN, Liu Q, Du YC, An YQ, Wang L, Chen EH. DARE: Disentanglement-augmented rationale extraction. In: Proc. of the 36th Int’l
                     Conf. on Neural Information Processing Systems. New Orleans: Curran Associates Inc., 2022. 1929.
                 [41]   Sun X, Wang L, Liu Q, Wu S, Wang ZL, Wang L. DIVE: Subgraph disagreement for graph out-of-distribution generalization. In: Proc. of
                     the 30th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining. Barcelona: ACM, 2024. 2794–2805. [doi: 10.1145/3637528.
                     3671878]
                 [42]   Yang YH, Wu L, Liao YX, He ZZ, Shao PY, Hong RC, Wang M. Invariance matters: Empowering social recommendation via graph
                     invariant learning. In: Proc. of the 48th Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval. Padua: ACM,
                     2025. 2038–2047. [doi: 10.1145/3726302.3730013]
                 [43]   Yue LN, Liu Q, Du YC, Wang L, An YQ, Chen EH. Towards reliable and faithful explanations: A disentanglement-augmented approach
                     for selective rationalization. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2025, 47(11): 9906–9921. [doi: 10.1109/TPAMI.
                     2025.3592313]
                 [44]   Xu Z, Pan MH, Chen YZ, Chen HY, Yan YC, Das M, Tong HH. Fine-grained graph rationalization. In: Proc. of the 34th ACM Int’l
                     Conf. on Information and Knowledge Management. Seoul: ACM, 2025. 3708–3719. [doi: 10.1145/3746252.3761307]
                 [45]   Piao YH, Lee S, Lu YJX, Kim S. Improving out-of-distribution generalization in graphs via hierarchical semantic environments. In: Proc.
                     of  the  2024  IEEE/CVF  Conf.  on  Computer  Vision  and  Pattern  Recognition.  Seattle:  IEEE,  2024.  27631–27640.  [doi:  10.1109/
                     CVPR52733.2024.02609]
                 [46]   Yao TJ, Chen YQ, Chen ZH, Hu K, Shen ZQ, Zhang K. Empowering graph invariance learning with deep spurious infomax. In: Proc. of
                     the 41st Int’l Conf. on Machine Learning. PMLR, 2024. 56860–56884.
                 [47]   Jang E, Gu SX, Poole B. Categorical reparametrization with Gumble-Softmax. arXiv:1611.01144, 2016.
                 [48]   Tishby N, Pereira FC, Bialek W. The information bottleneck method. arXiv:physics/0004057, 2000.
                 [49]   Alemi  AA,  Fischer  I,  Dillon  JV,  Murphy  K.  Deep  variational  information  bottleneck.  In:  Proc.  of  the  5th  Int’l  Conf.  on  Learning
                     Representations. Toulon: OpenReview.net, 2017.
                 [50]   Pearl J, Glymour M, Jewell NP. Causal Inference in Statistics: A Primer. Chichester: John Wiley & Sons, 2016.
                 [51]   Wang T, Huang JQ, Zhang HW, Sun QR. Visual commonsense R-CNN. In: Proc. of the 2020 IEEE/CVF Conf. on Computer Vision and
                     Pattern Recognition. Seattle: IEEE, 2020. 10760–10770. [doi: 10.1109/CVPR42600.2020.01077]
                 [52]   Xu  K,  Hu  W,  Leskovec  J,  Jegelka  S.  How  powerful  are  graph  neural  networks?  In:  Proc.  of  the  7th  Int’l  Conf.  on  Learning
                     Representations. New Orleans: OpenReview.net, 2019.
                 [53]   Kingma DP, Ba J. Adam: A method for stochastic optimization. In: Proc. of the 3rd Int’l Conf. on Learning Representations. San Diego:
                     ICLR, 2015.
                 [54]   Li QM, Han ZC, Wu XM. Deeper insights into graph convolutional networks for semi-supervised learning. In: Proc. of the 32nd AAAI
                     Conf. on Artificial Intelligence. New Orleans: AAAI Press, 2018. 433.


                 附中文参考文献
                 [26]   赵港, 王千阁, 姚烽, 张岩峰, 于戈. 大规模图神经网络系统综述. 软件学报, 2022, 33(1): 150–170. http://www.jos.org.cn/1000-9825/
                     6311.htm [doi: 10.13328/j.cnki.jos.006311]
                 [31]   周纯英, 曾诚, 何鹏, 张龑. GKCI: 改进的基于图神经网络的关键类识别方法. 软件学报, 2023, 34(6): 2509–2525. http://www.jos.org.
                     cn/1000-9825/6846.htm [doi: 10.13328/j.cnki.jos.006846]
                 [38]   李平, 宋舒寒, 张园, 曹华伟, 叶笑春, 唐志敏. HSEGRL: 一种分层可自解释的图表示学习模型. 计算机研究与发展, 2024, 61(8):
                     1993–2007. [doi: 10.7544/issn1000-1239.202440142]
   261   262   263   264   265   266   267   268   269   270   271