Computer Science ›› 2022, Vol. 49 ›› Issue (3): 129-133.doi: 10.11896/jsjkx.201100152
• Database & Big Data & Data Science • Previous Articles Next Articles
ZHOU Hai-yu, ZHANG Dao-qiang
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| [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,SZLAM A.Yann LeCun:SpectralNetworks and Locally Connected Networks on Graphs[C]//International Conference on Learning Representations.2014. [3]DEFFERRARD M,BRESSON X,VANDERGHEYNST P.Convolutional 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[C]//International Conference on Learning Representations.2017. [5]ATWOOD J,TOWSLEY D.Diffusion convolutional neural networks[C]//Advances in Neural Information Processing Systems.2016:1993-2001. [6]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. [7]HAMILTON W,YING Z T,LESKOVEC J.Inductive representation learning on large graphs [C]//Advances in Neural Information Processing Systems.2017:1024-1034. [8]KTENA S I,PARISOT S,FERRANTE E,et al.Distance metric learning using graph convolutional networks:Application to functional brain networks [C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Springer,2017:469-477. [9]PARISOT S,KTENA S I,FERRANTE E,et al.Disease predic-tion using graph convolutional networks:application to autism spectrum disorder and Alzheimer’s disease[J].Medical Image Analysis,2018,48:117-130. [10]HUANG Y C,LIU Q S,METAXAS D.Video object segmentation by hypergraph cut [C]//IEEE Conference on Computer Vision and Pattern Recognition.2009:1738-1745. [11]ZHANG Z Z,LIN H J,GAO Y,et al.Dynamic HypergraphStructure Learning [C]//Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence.2018:3162-3169. [12]FENG Y F,YOU H X,ZHANG Z Z,et al.Hypergraph neural networks [C]//Proceedings of the AAAI Conference on Artificial Intelligence.2019:3558-3565. [13]BAI S,ZHANG F H,TORR P H S.Hypergraph convolutionand hypergraph attention[J].arXiv:1901.08150,2019. [14]JIANG J W,WEI Y X,FENG Y F,et al.Dynamic hypergraph neural networks[C]//Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence.2019:2635-2641. [15]MARTINO A D,YAN C G,LI Q,et al.The autism brain ima-ging data exchange:towards a large-scale evaluation of the intrinsic brain architecture in autism[J].Molecular Psychiatry,2014,19(6):659-667. [16]MAO B C,HUANG J S,ZHANG D Q.Node based row filter convolutional neural network for brain network classification [C]//Pacific Rim International Conference on Artificial Intelligence.2018:1069-1080. [17]KTENA S I,PARISOT S,FERRANTE E,et al.Metric learning with spectral graph convolutions on brain connectivity networks[J].NeuroImage,2018,169:431-442. |
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