Computer Science ›› 2020, Vol. 47 ›› Issue (9): 52-59.doi: 10.11896/jsjkx.190300004
Special Issue: Big Data & Data Scinece
• Database & Big Data & Data Science • Previous Articles Next Articles
DING Yu, WEI Hao, PAN Zhi-song, LIU Xin
CLC Number:
[1] JOLLIFFE I T.Pincipal Component Analysis[J].Journal ofMarketing Research,2002,25(4):513. [2] ROWEIS S T,SAUL L K.Nonlinear dimensionality reduction by locally linear embedding[J].Science,2000,290:2323-2326. [3] SAUL L K,ROWEIS S T.An introduction to locally linear embedding[J].Journal of Machine Learning Research,2008,7. [4] BELKIN M,NIYOGI P.Laplacian eigenmaps and spectral techniques for embedding and clustering[C]//Proceedings of the 14th International Conference on Neural Information Processing Systems:Natural and Synthetic.Vancouver,2001:585-591. [5] TANG L,LIU H.Leveraging social media networks for classification[J].Data Min Knowl Discov,2011,23:447-478. [6] CHEN M,YANG Q,TANG X.Directed graph embedding[C]//Proceedings of the 20th International Joint Conference on Artifical Intelligence.Hyderabad,2007:2707-2712. [7] MIKOLOV T,SUTSKEVER I,CHEN K,et al.Distributed representations of words and phrases and their compositionality[C]//Proceedings of Advances in Neural Information Proces-sing Systems.Lake Tahoe,2013:3111-3119. [8] MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[J].arXiv:1301.3781. [9] MIKOLOV T,KARAFIAT M,BURGET L,et al.Recurrentneural network based language model[C]//Proceedings of International Speech Communication Association.2010:1045-1048. [10] PEROZZI B,AL-RFOU R,SKIENA S.Deepwalk:online learning of social representations[C]//The ACM SIGKDD International Conference.ACM,2014:701-710. [11] GROVER A,LESKOVEC J.node2vec:scalable feature learning for networks[C]//The ACM SIGKDD International Conference.2016. [12] CHENG W,GREAVES C,WARREN M.From n-gram to skip-gram to concgram[J].International Journal of Corpus Linguistics,2006,11(4):411-433. [13] TANG J,QU M,WANG M,et al.Line:large-scale information network embedding[C]//International Conference on World Wide Web.2015:1067-1077. [14] CAO S,LU W,XU Q.Grarep:Learning graph representations with global structural information[C]//KDD.2015. [15] ZHANG Z,CUI P,WANG X,et al.Arbitrary-order proximity preserved network embedding[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.ACM,2018:2778-2786. [16] OU M,CUI P,PEI J,et al.Asymmetric transitivity preserving graph embedding[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Francisco,2016,1:1105-1114. [17] MA J,CUI P,WANG X,et al.Hierarchical taxonomy aware network embedding[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.ACM,2018:1920-1929. [18] WANG X,CUI P,WANG J,et al.Community Preserving Network Embedding[C]//AAAI.2017. [19] CAVALLARI S,ZHENG V W,CAI H,et al.Learning community embedding with community detection and node embedding on graphs[C]//Proceedings of CIKM.2017. [20] TU C C,ZENG X K,WANG H,et al.A Unified Framework for Community Detection and Network Representation Learning[J].arXiv:1611.06645. [21] LE T M,LAUW H W.Probabilistic latent document network embedding[C]// 2014 IEEE International Conference on Data Mining (ICDM).IEEE,2014:270-279. [22] CHANG J,BLEI D M.Relational topic models for document networks[C]//International Conference on Artificial Intelligence and Statistics.2009:81-88. [23] YANG C,LIU Z,ZHAO D,et al.Network representation learning with rich text information[C]//International Conference on Artificial Intelligence.2015:2111-2117. [24] NATARAJAN N,DHILLON I S.Inductive matrix completion for predicting gene-disease associations[J].Bioinformatics,2014,30(12):i60-i68. [25] AHMED N K,ROSSI R A,ZHOU R,et al.Inductive Representation Learning in Large Attributed Graphs[J].arXiv:1710.09471v2,2017. [26] AHMED N K,ROSSI R A,ZHOU R,et al.A Framework for Generalizing Graph-based Representation Learning Methods[J].arXiv:1709.04596,2017. [27] NGUYEN D,MALLIAROS F D.BiasedWalk:Biased Sampling for Representation Learning on Graphs[J].arXiv:1809.02482,2018. [28] TU C C,LIU H,LIU Z Y,et al.CANE:context-aware network embedding for relation modeling[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.Vancouve,2017:1722-1731. [29] PAN S,WU J,ZHU X,et al.Tri-Party Deep Network Representation[C]// The 25th International Joint Conference on Artificial Intelligence (IJCAI-2016).AAAI Press,2016. [30] ZHU J,AHMED A,XING E P.Medlda:maximum margin supervised topic models[J].JMLR,2012,13(1):2237-2278. [31] TU C C,ZHANG W C,LIU Z Y,et al.Max-Margin DeepWalk:discriminative learning of network representation[C]//Proceedings of International Joint Conference on Artificial Intelligence (IJCAI).2016. [32] HEARST M A,DUMAIS S T,OSMAN E,et al.Support vector machines[J].IEEE Intelligent Systems and their Applications,1998,13(4):18-28. [33] HUANG X,LI J,HU X.Label Informed Attributed Network Embedding[C]// Tenth Acm International Conference on Web Search & Data Mining.ACM,2017. [34] BELKIN M,NIYOGI P.Laplacian eigenmaps and spectral techniques for embedding and clustering[J].Advances in Neural Information Processing Systems,2001,14(6):585-591. [35] LIU J,HE Z C,WEI L,et al.Content to Node:Self-translation Network Embedding[C]//SIGKDD.2018. [36] HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation1997,9(8):1735-1780. [37] WANG D X,CUI P,ZHU W W.Structural deep network embedding[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2016:1225-1234. [38] ZHANG Z,YANG H X,BU J J,et al.ANRL:Attributed Network Representation Learning via Deep Neural Networks[C]//IJCAI.2018. [39] KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[J].arXiv:1609.02907,2016. [40] KIPF T N,WELLING M.Variational graph auto-encoders[J].arXiv:1611.07308,2016. [41] KINGMA D P,WELLING M.Auto-encoding variational bayes[C]//Proceedings of the International Conference on Learning Representations (ICLR).2014. [42] WANG S,TANG J,AGGARWAL C,et al.Signed network embedding in social media[C]//Proceedings of the 2017 SIAM International Conference on Data Mining.SIAM,2017:327-335. [43] TU K,CUI P,WANG X,et al.Deep recursive network embedding with regular equivalence[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.ACM,2018:2357-2366. [44] ZHU D,CUI P,WANG D,et al.Deep variational network embedding in wasserstein space[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.ACM,2018:2827-2836. [45] VELIKOVI P,CUCURULL G,CASANOVA A,et al.Graph attention networks[J].arXiv:1710.10903,2017. [46] HAMILTON W,YING Z,LESKOVEC J.Inductive representa-.tion learning on large graphs[C]//Advances in Neural Information Processing Systems.2017:1024-1034. [47] KULKARNI V,AL-RFOU R,PEROZZI B,et al.Statistically Significant Detection of Linguistic Change[C]// Proceedings of the 24th International Conference on World Wide Web.ACM,2014:625-635. [48] HAMILTON W L,LESKOVEC J,JURAFSKY D,et al.Diachronic word embeddings reveal statistical laws of semantic change[J].Meeting of the Association for Computational Linguistics,2016,1:1489-1501. [49] LI J,DANI H,HU X,et al.Attributed Network Embedding for Learning in a Dynamic Environment[C]// Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.ACM,2017:387-396. [50] STEWART G W.Matrix Perturbation Theory[M]//MatrixPerturbation Theory.1990. [51] HARDOON D R,SZEDMAK S,SHAWE-TAYLOR J.Canonical correlation analysis:An overview with application to learning methods[J].Neural computation,2004,16(12):2639-2664. [52] GOYAL P,KAMRA N,HE X,et al.Dyngem:Deep embedding method for dynamic graphs[J].arXiv:1805.11273,2018. [53] CHANG S,HAN W,TANG J,et al.Heterogeneous networkembedding via deep architectures[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.ACM,2015:119-128. [54] NGUYEN G H,LEE J B,ROSSI R A,et al.Continuous-time dynamic network embeddings[C]//Companion of the The Web Conference 2018 on The Web Conference 2018.2018:969-976. [55] ZHOU L,YANG Y,REN X,et al.Dynamic Network Embedding by Modeling Triadic Closure Process[C]//AAAI.2018. [56] LUN D,YUN W,GUOJIE S,et al.Dynamic Network Embedding:An Extended Approach for Skip-gram based Network Embedding[C]// IJCAI.2018. [57] ZHU D,CUI P,ZHANG Z,et al.High-order Proximity Preserved Embedding For Dynamic Networks[J].IEEE Transactions on Knowledge & Data Engineering,2018,30(11):2132-2144. |
[1] | RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207. |
[2] | LENG Dian-dian, DU Peng, CHEN Jian-ting, XIANG Yang. Automated Container Terminal Oriented Travel Time Estimation of AGV [J]. Computer Science, 2022, 49(9): 208-214. |
[3] | GUO Peng-jun, ZHANG Jing-zhou, YANG Yuan-fan, YANG Shen-xiang. Study on Wireless Communication Network Architecture and Access Control Algorithm in Aircraft [J]. Computer Science, 2022, 49(9): 268-274. |
[4] | NING Han-yang, MA Miao, YANG Bo, LIU Shi-chang. Research Progress and Analysis on Intelligent Cryptology [J]. Computer Science, 2022, 49(9): 288-296. |
[5] | TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305. |
[6] | HUANG Li, ZHU Yan, LI Chun-ping. Author’s Academic Behavior Prediction Based on Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(9): 76-82. |
[7] | XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171. |
[8] | LI Yao, LI Tao, LI Qi-fan, LIANG Jia-rui, Ibegbu Nnamdi JULIAN, CHEN Jun-jie, GUO Hao. Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network [J]. Computer Science, 2022, 49(8): 257-266. |
[9] | LI Zong-min, ZHANG Yu-peng, LIU Yu-jie, LI Hua. Deformable Graph Convolutional Networks Based Point Cloud Representation Learning [J]. Computer Science, 2022, 49(8): 273-278. |
[10] | WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293. |
[11] | HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329. |
[12] | JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335. |
[13] | ZHANG Guang-hua, GAO Tian-jiao, CHEN Zhen-guo, YU Nai-wen. Study on Malware Classification Based on N-Gram Static Analysis Technology [J]. Computer Science, 2022, 49(8): 336-343. |
[14] | ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119. |
[15] | SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177. |
|