[1]常新功,王金珏.基于图卷积集成的网络表示学习[J].智能系统学报,2022,17(3):547-555.[doi:10.11992/tis.202107048]
 CHANG Xingong,WANG Jinjue.Network representation learning using graph convolution ensemble[J].CAAI Transactions on Intelligent Systems,2022,17(3):547-555.[doi:10.11992/tis.202107048]
点击复制

基于图卷积集成的网络表示学习

参考文献/References:
[1] BHAGAT S, CORMODE G, MUTHUKRISHNAN S. Node Classification in Social Networks[M]// Social Network Data Analytics. Boston, MA: Springer, 2011: 115–148.
[2] LIBEN-NOWELL D, KLEINBERG J. The link-prediction problem for social networks[J]. Journal of the American society for information science and technology, 2007, 58(7): 1019–1031.
[3] CO?KUN M, KOYUTüRK M. Node similarity based graph convolution for link prediction in biological networks[J]. Bioinformatics (Oxford, England), 2021, 37(23): 4501–4508.
[4] DER MAATEN L V, HINTON G. Visualizing data using t-SNE[J]. Journal of machine learning research, 2008: 2579–2605.
[5] TANG Jian, LIU Jingzhou, ZHANG Ming, et al. Visualizing Large-Scale and High-Dimensional Data[C]//Proceedings of the 25th International Conference ompanion on World Wide Web. Canada, New York, 2016: 287–297.
[6] ZHANG Daokun, YIN Jie, ZHU Xingquan, et al. Network representation learning: a survey[J]. IEEE transactions on big data, 2020, 6(1): 3–28.
[7] BENGIO Y, COURVILLE A, VINCENT P. Representation learning: a review and new perspectives[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(8): 1798–1828.
[8] 尹赢, 吉立新, 黄瑞阳, 等. 网络表示学习的研究与发展[J]. 网络与信息安全学报, 2019, 5(2): 77–87
YIN Ying, JI Lixin, HUANG Ruiyang, et al. Research and development of network representation learning[J]. Chinese journal of network and information security, 2019, 5(2): 77–87
[9] ROWEIS S T, SAUL L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500): 2323–2326.
[10] HE Xiaofei, Niyogi P. Locality preserving projections[J]. In advances in neural information processing systems, 2004, 16: 153–160.
[11] TU Cunchao, ZHANG Weicheng, LIU Zhiyuan, et al. Max-margin DeepWalk: discriminative learning of network representation[C]//Proceedings of the 25th Inter-national Joint Conference on Artifificial Intelligence. New York: ACM, 2016: 3889–3895.
[12] CAO Shaosheng, LU Wei, XU Qiongkai. Grarep: Lear- ning graph representations with global structural informat- ion[C]// Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Melbourne, Australia, 2015: 891–900.
[13] PEROZZI B, AL-RFOU R, SKIENA S. DeepWalk: online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York : ACM, 2014: 701–710.
[14] GROVER A, LESKOVEC J. node2vec: scalable feature learning for networks[EB/OL]. (2016–07–03)[ 2021–07–23]https://arxiv.org/abs/1607.00653.
[15] TANG Jian, QU Meng, WANG Mingzhe, et al. LINE: large-scale information network embedding[C]//Procee- dings of the 24th International Conference on World Wide Web. New York: ACM, 2015: 1067–1077.
[16] WANG Daixin, CUI Peng, ZHU Wenwu. Structural deep network embedding[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA, 2016: 1225–1234.
[17] HAMILTON W, YING Z, LESKOVEC J. Inductive repres- entation learning on large graphs[C]//Advances in Neural Information Processing Systems. Long Beach , USA, 2017: 1024–1034.
[18] WANG Hongwei, WANG Jia , WANG Jialin, et al. Graph- GAN: graph representation learning with generative adve- rsarial nets[C]// Proceedings of the 32th AAAI Conference on Artificial Intelligence, New Orleans, USA, 2018: 2508–2515.
[19] ZHANG Boyu, IANG Ji, WANG Xin. Network representation learning with ensemble methods[J]. Neur- ocomputing, 2020, 380: 141–149.
[20] 徐冰冰, 岑科廷, 黄俊杰, 等. 图卷积神经网络综述[J]. 计算机学报, 2020, 43(5): 755–780
XU Bingbing, CEN Keting, HUANG Junjie, et al. A survey on graph convolutional neural network[J]. Chinese journal of computers, 2020, 43(5): 755–780
[21] KIPF T N, WELLING M . Semi-supervised classifificati- on with graph convolutional networks[EB/OL]. (2016–09–09)[ 2021–07–23]https://arxiv.org/abs/1609.02907.
[22] 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016.
[23] MIKOLOV T, SUTSKEVER I, CHEN KAI, et al. Distributed representations of words and phrases and their compositionality[EB/OL]. (2013–19–16)[ 2021–07–23]https://arxiv.org/abs/1310.4546v1.
[24] SUN ZHIQING, DENG ZHI-HONG, NIE JIAN-YUN, et al. RotatE: knowledge graph embedding by relational rotation in complex space[EB/OL]. (2019–02–26)[ 2021–07-23]https://arxiv.org/abs/1902.10197v1.
相似文献/References:
[1]胡小生,温菊屏,钟勇.动态平衡采样的不平衡数据集成分类方法[J].智能系统学报,2016,11(2):257.[doi:10.11992/tis.201507015]
 HU Xiaosheng,WEN Juping,ZHONG Yong.Imbalanced data ensemble classification using dynamic balance sampling[J].CAAI Transactions on Intelligent Systems,2016,11():257.[doi:10.11992/tis.201507015]
[2]闵帆,王宏杰,刘福伦,等.SUCE:基于聚类集成的半监督二分类方法[J].智能系统学报,2018,13(6):974.[doi:10.11992/tis.201711027]
 MIN Fan,WANG Hongjie,LIU Fulun,et al.SUCE: semi-supervised binary classification based on clustering ensemble[J].CAAI Transactions on Intelligent Systems,2018,13():974.[doi:10.11992/tis.201711027]
[3]张本才,王志海,孙艳歌.一种多样性和精度加权的数据流集成分类算法[J].智能系统学报,2019,14(1):179.[doi:10.11992/tis.201806021]
 ZHANG Bencai,WANG Zhihai,SUN Yange.An ensemble classification algorithm based on diversity and accuracy weighting for data streams[J].CAAI Transactions on Intelligent Systems,2019,14():179.[doi:10.11992/tis.201806021]
[4]常亮,张伟涛,古天龙,等.知识图谱的推荐系统综述[J].智能系统学报,2019,14(2):207.[doi:10.11992/tis.201805001]
 CHANG Liang,ZHANG Weitao,GU Tianlong,et al.Review of recommendation systems based on knowledge graph[J].CAAI Transactions on Intelligent Systems,2019,14():207.[doi:10.11992/tis.201805001]
[5]张燕,杜红乐.基于异构距离的集成分类算法研究[J].智能系统学报,2019,14(4):733.[doi:10.11992/tis.201807023]
 ZHANG Yan,DU Hongle.Imbalanced heterogeneous data ensemble classification based on HVDM-KNN[J].CAAI Transactions on Intelligent Systems,2019,14():733.[doi:10.11992/tis.201807023]
[6]张潇鲲,刘琰,陈静.引入外部词向量的文本信息网络表示学习[J].智能系统学报,2019,14(5):1056.[doi:10.11992/tis.201809037]
 ZHANG Xiaokun,LIU Yan,CHEN Jing.Representation learning using network embedding based on external word vectors[J].CAAI Transactions on Intelligent Systems,2019,14():1056.[doi:10.11992/tis.201809037]
[7]张蕾,钱峰,赵姝,等.基于多粒度结构的网络表示学习[J].智能系统学报,2019,14(6):1233.[doi:10.11992/tis.201905045]
 ZHANG Lei,QIAN Feng,ZHAO Shu,et al.Network representation learning based on multi-granularity structure[J].CAAI Transactions on Intelligent Systems,2019,14():1233.[doi:10.11992/tis.201905045]
[8]贾中浩,宾辰忠,古天龙,等.基于知识图谱和用户长短期偏好的个性化景点推荐[J].智能系统学报,2020,15(5):990.[doi:10.11992/tis.201904064]
 JIA Zhonghao,BIN Chenzhong,GU Tianlong,et al.Personalized attraction recommendation based on the knowledge graph and users’ long-term and short-term preferences[J].CAAI Transactions on Intelligent Systems,2020,15():990.[doi:10.11992/tis.201904064]
[9]闫涵,张旭秀,张净丹.多感知兴趣区域特征融合的图像识别方法[J].智能系统学报,2021,16(2):263.[doi:10.11992/tis.201906032]
 YAN Han,ZHANG Xuxiu,ZHANG Jingdan.Image recognition method based on multi-perceptual interest region feature fusion[J].CAAI Transactions on Intelligent Systems,2021,16():263.[doi:10.11992/tis.201906032]
[10]汤礼颖,贺利乐,何林,等.一种卷积神经网络集成的多样性度量方法[J].智能系统学报,2021,16(6):1030.[doi:10.11992/tis.202011023]
 TANG Liying,HE Lile,HE Lin,et al.Diversity measuring method of a convolutional neural network ensemble[J].CAAI Transactions on Intelligent Systems,2021,16():1030.[doi:10.11992/tis.202011023]

备注/Memo

收稿日期:2021-07-23。
基金项目:国家自然科学基金项目(61906110);山西财经大学研究生创新项目(21cxxj088).
作者简介:常新功,教授,博士,CCF高级会员,主要研究方向为图神经网络、数据挖掘、进化算法。主持10项山西省重点课题。发表学术论文30余篇;王金珏,硕士研究生,主要研究方向为图神经网络、数据挖掘
通讯作者:常新功.E-mail:c_x_g@126.com

更新日期/Last Update: 1900-01-01
Copyright © 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134 邮箱:tis@vip.sina.com