Page 407 - 《软件学报》2025年第5期

王晨旭 等: 基于半监督和自监督图表示学习的恶意节点检测 2307

PMLR, 2019. 21–29.
[17] Zhu J, Yan YJ, Zhao LX, Heimann M, Akoglu L, Koutra D. Beyond homophily in graph neural networks: Current limitations and
effective designs. In: Proc. of the 34th Int’l Conf. on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2020.
7793–7804.
[18] Xu K, Li CT, Tian YL, Sonobe T, Kawarabayashi K, Jegelka S. Representation learning on graphs with jumping knowledge networks. In:
Proc. of the 35th Int’l Conf. on Machine Learning. Stockholm: PMLR, 2018. 5453–5462.
[19] Cao KD, Wei C, Gaidon A, Arechiga N, Ma TY. Learning imbalanced datasets with label-distribution-aware margin loss. In: Proc. of the
33rd Int’l Conf. on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2019. 1567–1578.
[20] Cui Y, Jia ML, Lin TY, Song Y, Belongie S. Class-balanced loss based on effective number of samples. In: Proc. of the 2019 IEEE/CVF
Conf. on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019. 9260–9269. [doi: 10.1109/CVPR.2019.00949]
[21] Hassani K, Khasahmadi AH. Contrastive multi-view representation learning on graphs. In: Proc. of the 37th Int’l Conf. on Machine
Learning. Online: JMLR.org, 2020. 4116–4126.
[22] Zhang CS, Chen J, Li QL, Deng BQ, Wang J, Chen CG. Deep contrastive learning: A survey. Acta Automatica Sinica, 2023, 49(1):
15–39 (in Chinese with English abstract). [doi: 10.16383/j.aas.c220421]
[23] Zhu JG, Wang Z, Chen JJ, Chen YPP, Jiang YG. Balanced contrastive learning for long-tailed visual recognition. In: Proc. of the 2022
IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 6908–6917. [doi: 10.1109/CVPR52688.2022.
00678]
[24] Jiang M. Catching social media advertisers with strategy analysis. In: Proc. of the 1st Int’l Workshop on Computational Methods for
CyberSafety. Indianapolis: ACM, 2016. 5–10. [doi: 10.1145/3002137.3002143]
[25] Gao Y, Wang X, He XN, Liu ZG, Feng HM, Zhang YD. Addressing heterophily in graph anomaly detection: A perspective of graph
spectrum. In: Proc. of the 2023 Web Conf. Austin: ACM, 2023. 1528–1538. [doi: 10.1145/3543507.3583268]
[26] 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. Vancouver: OpenReview.net, 2018.
[27] Gasteiger J, Bojchevski A, Günnemann S. Predict then propagate: Graph neural networks meet personalized PageRank. In: Proc. of the
2019 Int’l Conf. on Learning Representations. New Orleans: OpenReview.net, 2019.
[28] Chien E, Peng JH, Li P, Milenkovic O. Adaptive universal generalized PageRank graph neural network. In: Proc. of the 2021 Int’l Conf.
on Learning Representations. Vienna: OpenReview.net, 2021.

附中文参考文献:
[22] 张重生, 陈杰, 李岐龙, 邓斌权, 王杰, 陈承功. 深度对比学习综述. 自动化学报, 2023, 49(1): 15–39. [doi: 10.16383/j.aas.c220421]

王晨旭(1986－), 男, 博士, 副教授, CCF 高级会 王梦勤(2000－), 女, 硕士生, 主要研究领域为财
员, 主要研究领域为网络数据挖掘与网络安全, 务欺诈检测.
数据安全, 区块链.

王凯月(1995－), 女, 硕士, 主要研究领域为图神
经网络, 恶意节点检测.