Page 235 - 《软件学报》2026年第2期

714 软件学报 2026 年第 37 卷第 2 期

1998, 20(1): 359–392. [doi: 10.1137/S1064827595287997]
[34] Belkin M, Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Proc. of the 15th Int’l Conf. on
Neural Information Processing Systems: Natural and Synthetic. Vancouver: MIT Press, 2001. 585–591.
[35] Namata G, London B, Getoor L, Huang B. Query-driven active surveying for collective classification. In: Proc. of the 2012 Workshop on
Mining and Learning with Graphs. Edinburgh, 2012.
[36] McCallum AK, Nigam K, Rennie J, Seymore K. Automating the construction of Internet portals with machine learning. Information
Retrieval, 2000, 3(2): 127–163. [doi: 10.1023/A:1009953814988]
[37] Sen P, Namata G, Bilgic M, Getoor L, Gallagher B, Eliassi-Rad T. Collective classification in network data. AI Magazine, 2008, 29(3):
93–106. [doi: 10.1609/aimag.v29i3.2157]
[38] Zitnik M, Leskovec J. Predicting multicellular function through multi-layer tissue networks. Bioinformatics, 2017, 33(14): i190–i198.
[doi: 10.1093/bioinformatics/btx252]
[39] Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. In: Proc. of the 5th Int’l Conf. on Learning
Representations. Toulon: OpenReview.net, 2017.
[40] Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y. Graph attention networks. arXiv:1710.10903, 2017.
[41] Hamilton WL, Ying R, Leskovec J. Inductive representation learning on large graphs. In: Proc. of the 31st Int’l Conf. on Neural
Information Processing Systems. Long Beach: Curran Associates Inc., 2017. 1025–1035.
[42] 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]
[43] Veličković P, Fedus W, Hamilton WL, Liò P, Bengio Y, Hjelm RD. Deep graph infomax. arXiv:1809.10341, 2018.
[44] Thakoor S, Tallec C, Azar MG, Azabou M, Dyer EL, Munos R, Veličković P, Valko M. Large-scale representation learning on graphs via
bootstrapping. In: Proc. of the 10th Int’l Conf. on Learning Representations. OpenReview.net, 2022.
[45] 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]
[46] Xu DK, Cheng W, Luo DS, Chen HF, Zhang X. InfoGCL: Information-aware graph contrastive learning. In: Proc. of the 35th Int’l Conf.
on Neural Information Processing Systems. Curran Associates Inc., 2021. 2327.
[47] Zhang HR, Wu QT, Yan JC, Wipf D, Yu PS. From canonical correlation analysis to self-supervised graph neural networks. In: Proc. of
the 35th Int’l Conf. on Neural Information Processing Systems. Curran Associates Inc., 2021.
[48] Xu K, Hu WH, 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.
[49] Ying Z, You JX, Morris C, Ren X, Hamilton WL, Leskovec J. Hierarchical graph representation learning with differentiable pooling. In:
Proc. of the 32nd Int’l Conf. on Neural Information Processing Systems. Montréal: Curran Associates Inc., 2018. 4805–4815.
[50] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. In: Proc. of the 5th IEEE Int’l Conf. on Data Mining. Houston: IEEE, 2005.
8. [doi: 10.1109/ICDM.2005.132]
[51] Shervashidze N, Vishwanathan SVN, Petri T, Mehlhorn K, Borgwardt KM. Efficient graphlet kernels for large graph comparison. In:
Proc. of the 12th Int’l Conf. on Artificial Intelligence and Statistics. Clearwater Beach: JMLR.org, 2009. 488–495.
[52] Shervashidze N, Schweitzer P, van Leeuwen EJ, Mehlhorn K, Borgwardt KM. Weisfeiler-lehman graph kernels. Journal of Machine
Learning Research, 2011, 12: 2539–2561.
[53] Yanardag P, Vishwanathan SVN. Deep graph kernels. In: Proc. of the 21st ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data
Mining. Sydney: ACM, 2015. 1365–1374. [doi: 10.1145/2783258.2783417]
[54] Kondor R, Pan H. The multiscale laplacian graph kernel. In: Proc. of the 30th Int’l Conf. on Neural Information Processing Systems.
Barcelona: Curran Associates Inc., 2016. 2990–2998.
[55] Narayanan A, Chandramohan M, Venkatesan R, Chen LH, Liu Y, Jaiswal S. graph2vec: Learning distributed representations of graphs.
arXiv:1707.05005, 2017.
[56] Sun FY, Hoffmann J, Verma V, Tang J. InfoGraph: Unsupervised and semi-supervised graph-level representation learning via mutual
information maximization. In: Proc. of the 8th Int’l Conf. on Learning Representations. Addis Ababa: OpenReview.net, 2020.
[57] You YN, Chen TL, Shen Y, Wang ZY. Graph contrastive learning automated. In: Proc. of the 38th Int’l Conf. on Machine Learning.
PMLR, 2021. 12121–12132.
[58] Hassani K, Ahmadi AHK. Contrastive multi-view representation learning on graphs. In: Proc. of the 37th Int’l Conf. on Machine
Learning. PMLR, 2020. 4116–4126.
[59] You YN, Chen TL, Sui YD, Chen T, Wang ZY, Shen Y. Graph contrastive learning with augmentations. In: Proc. of the 34th Int’l Conf.