计算机科学 ›› 2020, Vol. 47 ›› Issue (9): 219-225.doi: 10.11896/jsjkx.190900044
刘海潮1, 王莉2
LIU Hai-chao1, WANG Li2
摘要: 针对提取图表征用于图分类过程中的结构信息提取过程的问题,提出了一种图卷积神经网络与胶囊网络融合的图分类模型。首先,利用图卷积神经网络处理图中的节点信息,迭代以后得到节点表征,表征中蕴含着该节点的子树结构信息;然后,利用Weisfeiler-Lehman图核算法的思想对节点表征的多维度进行排序,得到多视角的图表征;最后,将多视角的图表征整理成胶囊的形式并输入胶囊网络,使用动态路由算法得到更高层次的分类胶囊,进而进行分类。实验结果表明,所提模型在公共数据集上的分类准确度提升了1%~3%,同时具备更强的结构特征提取能力,在少样本情况下的表现比DGCNN更加稳定。
中图分类号:
[1] SCARSELLI F,GORI M,TSOI A C,et al.The graph neuralnetwork model[J].IEEE Transactions on Neural Networks,2009,20(1):61-80. [2] BRUNA J,ZAREMBA W,SZLAMA,et al.Spectral networksand locally connected networks on graphs[J].arXiv:1312.6203,2013. [3] DEFFERRARD M,BRESSON X,VANDERGHEYNST P.Con-volutional neural networks on graphs with fast localized spectral filtering[C]//Advances in Neural Information Processing Systems.2016:3844-3852. [4] KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[J].arXiv:1609.02907,2016. [5] DUVENAUD D K,MACLAURIN D,IPARRAGUIRRE J,et al.Convolutional networks on graphs for learning molecular fingerprints[C]//Advances in Neural Information Processing Systems.2015:2224-2232. [6] NIEPERT M,AHMED M,KUTZKOV K.Learning convolu-tional neural networks for graphs[C]//International Conference on Machine Learning.2016:2014-2023. [7] ZHANG M,CUI Z,NEUMANNM,et al.An end-to-end deep learning architecture for graph classification[C]//Thirty-Se-cond AAAI Conference on Artificial Intelligence.2018. [8] SABOUR S,FROSST N,HINTONG E.Dynamic routing be-tween capsules[C]//Advances in Neural Information Proces-sing Systems.2017:3856-3866. [9] BAI L,XU L X,CUI L X,et al.Graph kernel in machine learning:recent works and future developments[J].Journal of Anhui University (Natural Science Edition),2017,41(1):21-28. [10] HAUSSLER D.Convolution kernels on discrete structures[R].Technical Report UCS-CRL-99-10,UC Santa Cruz,1999. [11] GÄRTNER T,FLACH P,WROBEL S.On graph kernels:Hardness results and efficient alternatives[M]//Learning Theo-ry and Kernel Machines.Berlin:Springer,2003:129-143. [12] BORGWARDT K M,KRIEGEL H P.Shortest-path kernels on graphs[C]//Fifth IEEE International Conference on Data Mi-ning (ICDM’05).IEEE,2005. [13] SHERVASHIDZE N,SCHWEITZER P,LEEUWEN E J,et al.Weisfeiler-lehman graph kernels[J].Journal of Machine Lear-ning Research,2011,12(Sep):2539-2561. [14] YING Z,YOU J,MORRISC,et al.Hierarchical graph represen-tation learning with differentiable pooling[C]//Advances in Neural Information Processing Systems.2018:4805-4815. [15] LEE J B,ROSSI R,KONG X.Graph classification using structural attention[C]//Proceedings of the 24th ACM SIGKDD International Conference on KnoWeisfeiler-Lehmanedge Discovery & Data Mining.ACM,2018:1666-1674. [16] VERMA S,ZHANG Z L.Graph capsule convolutional neural networks[J].arXiv:1805.08090,2018. [17] GAO H Y,WANG Z Y,JI S W.Large-scale learnable graphconvolutional networks[C]//Proceedings of the 24th ACM SIGKDD International Conference on KnoWeisfeiler-Lehmanedge Discovery & Data Mining.ACM,2018. [18] ZHOU J,CUI G,ZHANG Z,et al.Graph neural networks:A review of methods and applications[J].arXiv:1812.08434,2018. [19] SHERVASHIDZE N,VISHWANATHAN S,PETRI T,et al.Efficient graphlet kernels for large graph comparison[C]//AISTATS.2009:488-495. [20] ATWOOD J,TOWSLEY D.Diffusion-convolutional neural networks[C]//Advances in Neural Information Processing Systems.2016:1993-2001. |
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