[ "陈维政,男,北京大学博士生,主要研究方向为机器学习和社会网络分析。" ]
[ "张岩,男,北京大学教授、博士生导师,主要研究方向为信息检索、文本分析和数据挖掘。" ]
[ "李晓明,男,北京大学教授、博士生导师,主要研究方向为搜索引擎、网络数据挖掘和并行与分布式系统。" ]
网络首发:2015-06,
纸质出版:2015-06-20
移动端阅览
陈维政, 张岩, 李晓明. 网络表示学习[J]. 大数据, 2015,1(3):1-15.
Weizheng Chen, Yan Zhang, Xiaoming Li. Network Representation Learning[J]. BIG DATA RESEARCH, 2015, 1(3): 1-15.
陈维政, 张岩, 李晓明. 网络表示学习[J]. 大数据, 2015,1(3):1-15. DOI: 10.11959/j.issn.2096-0271.2015025.
Weizheng Chen, Yan Zhang, Xiaoming Li. Network Representation Learning[J]. BIG DATA RESEARCH, 2015, 1(3): 1-15. DOI: 10.11959/j.issn.2096-0271.2015025.
以Facebook、Twitter、微信和微博为代表的大型在线社会网络不断发展,产生了海量体现网络结构的数据。采用机器学习技术对网络数据进行分析的一个重要问题是如何对数据进行表示。首先介绍了网络表示学习的研究背景和相关定义。然后按照算法类别,介绍了当前5类主要的网络表示学习算法,特别地,对基于深度学习的网络表示学习技术进行了详细的介绍。之后讨论了网络表示学习的评测方法和应用场景。最后,探讨了网络表示学习的研究前景。
Along with the constant growth of massive online social networks such as Facebook
Weixin and Weibo
a tremendous amount of network data sets are generated.How to represent the data is an important aspect when we apply machine learning techniques to analyze network data sets.Firstly
the research background was introduced and the definitions of NRL (network representation learning) were related.According to the categories of different algorithms
five kinds of primary NRL algorithms were introduced.Particularly
a detailed introduction to NRL algorithms based deep learning techniques was given emphatically.Then the evaluation methods and application scenarios of NRL were discussed.Finally
the research prospect of NRL in the future was discussed.
Mairal J , Ponce J , Sapiro G , et al . Supervised dictionary learning . Proceedings of the 2009 Conference on Neural Information Processing Systems ,Vancouver,Canada, 2009 : 1033 ~ 1040 .
Roweis S T , Saul L K . Nonlinear dimensionality reduction by locally linear embedding . Science , 2000 , 290 ( 5 ): 2323 ~ 2326
yvärinen A , Oja E . Independent component analysis: algorithms and applications . Neural Networks , 2000 , 13 ( 4~5 ): 411 ~ 430
Lee H , Battle A , Rain R , et al . Efficient sparse coding algorithms . Proceedings of the 2006 Conference on Neural Information Processing Systems .Vancouver,Canada, 2006 : 801 ~ 808 .
Lee H , Battle A , Rain R . Representation learning: a review and new perspectives . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2013 , 35 ( 8 ): 1798 ~ 1828
Chen M , Yang Q , Tang X O . Directed graph embedding . Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI) ,Hyderabad,India, 2007 : 2707 ~ 2712
Kannan R , Vempala S . Spectral algorithms . Theoretical Computer Science , 2009 , 4 ( 3~4 ): 157 ~ 288
Brand M , Huang K . A unifying theorem for spectral embedding and clustering . Proceedings of the 9th International Conference on Workshop on Artificial Intelligence and Statistics ,Florida,USA, 2003
Le T , Lauw W . Probabilistic latent document network embedding . Proceedings of 2014 IEEE International Conference on Data Mining (ICDM) ,Shenzhen,China, 2014 : 270 ~ 279
Wojciech C , Brooks M J . A note on the locally linear embedding algorithm . International Journal of Pattern Recognition and Artificial Intelligence , 2009 , 23 ( 8 ): 1739 ~ 1752
Belkin M , Niyogi P . Laplacian eigenmaps and spectral techniques for embedding and clustering . Proceedings of Annual Conference on Neural Information Processing Systems(NIPS) ,Cambridge,UK, 2001 : 585 ~ 591
Tang L , Liu H . Relational learning via latent social dimensions . Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ,Paris,France, 2009 : 817 ~ 826
Newman M . Modularity and community structure in networks . Proceedings of the National Academy of Sciences , 2006 , 103 ( 23 ): 8577 ~ 8582
Zhou D Y , Huang J Y , Schölkopf B . Learning from labeled and unlabeled data on a directed graph . PProceedings of the 22nd International Conference on Machine Learning ,Bonn,Germany, 2005 : 1036 ~ 1043
Jacob Y , Denoyer L , Gallinari P . Learning latent representations of nodes for classifying in heterogeneous social networks . Proceedings of the 7th ACM International Conference on Web Search and Data Mining ,New York,USA, 2014 : 373 ~ 382
Yang J , Leskovec J . Modeling information diffusion in implicit networks . Proceedings of 2010 IEEE 10th International Conference on Data Mining (ICDM) ,Sydney,Australia, 2010 : 599 ~ 608
Bourigault S , Lagnier C , Lamprier S , et al . Learning social network embeddings for predicting information diffusion . Proceedings of the 7th ACM International Conference on Web Search and Data Mining ,New York,USA, 2014 : 393 ~ 402 .
Nallapati R , Ahmed A , Xing E , et al . Joint latent topic models for text and citations . Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ,Las Vegas,USA, 2008 : 542 ~ 550 .
Chang J , Blei D . Relational topic models for document networks . Proceedings of International Conference on Artificial Intelligence and Statistics ,Clearwater Beach,Florida,USA, 2009 : 81 ~ 88
Iwata T , Saito K , Ueda N , et al . Parametric embedding for class visualization . Neural Computation , 2007 , 19 ( 9 ): 2536 ~ 2556
Gopalan P , Blei D . Efficient discovery of overlapping communities in massive networks . Proceedings of the National Academy of Sciences , 2013 , 110 ( 36 ): 14534 ~ 14539
Gopalan P , Mimno D , Gerrish S , et al . JScalable inference of overlapping communities . Proceedings of the 2012 Conference on Neural Information Processing Systems ,Lake Tahoe,USA, 2012 : 2249 ~ 2257 .
Hu Z T , Yao J J , Cui B , et al . Community level diffusion extraction . Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data,Melbourne ,Victoria,Australia, 2015 : 1555 ~ 1569 .
Kobourov S . Spring embedders and force directed graph drawing algorithms . arXiv Preprint 2012 ,arXiv:1201.3011,2012
Fruchterman T , Reingold E . Graph drawing by force-directed placement . Software-Practice & Experience , 1991 , 21 ( 11 ): 1129 ~ 1164
Kamada T , Kawai S . An algorithm for drawing general undirected graphs . Information Processing Letters , 1989 , 31 ( 1 ): 7 ~ 15
Bastian M , Heymann S , Jacomy M . Gephi:an open source software for exploring and manipulating networks . Proceedings of the 3rd International Conference on Weblogs and Social Media ,San Jose,California,USA, 2009 : 361 ~ 362
Ellson J , Gansner E , Koutsofios L , et al . Graphviz-open source graph drawing tools . Graph Drawing .Berlin Heidelberg:Springer, 2002
Bengio Y , Goodfellow I , Courville A . Deep Learning . 2015
Perozzi B , Al-Rfou R , Skiena S . Deepwalk: online learning of social representations . Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining .New York,USA, 2014 : 701 ~ 710
Tang J , Qu M , Wang M Z , et al . LINE:large-scale information network embedding . Proceedings of the 24th International Conference on World Wide Web ,Florence,Italy, 2015 : 1067 ~ 1077
Mikolov T , Sutskever I , Chen K , et al . Distributed representations of words and phrases and their compositionality . Proceedings of the 2013 Conference onNeural Information Processing Systems ,Lake Tahoe,USA, 2013 : 3111 ~ 3119
Mikolov T , Chen K , Corrado G , et al . Efficient estimation of word representations in vector space . arXiv Preprint arXiv:1301.3781 2013
Mikolov T , Yih W T , Zweig G . Linguistic regularities in continuous space word representations . Proceedings of the 2013 Conference on NAACL and SEM ,Atlanta,USA, 2013 : 746 ~ 751
Hinton G E . Learning distributed representations of concepts . Proceedings of the Eighth Annual Conference on the Cognitive Science Society ,Amherst,Mass,USA, 1986 : 1 ~ 12
Bengio Y , Ducharme R , Vincent P , et al . A neural probabilistic language model . Journal of Machine Learning Research 2003 ( 3 ): 1137 ~ 1155
Morin F , Bengio Y . Proceedings of the 10th International Workshop Conference on Artificial Intelligence and Statistics . Journal of Machine Learning Research ,Barbados, 2005 : 246 ~ 252
Collober R , Weston J . A unified architecture for natural language processing: deep neural networks with multitask learning . Proceedings of the 25th International Conference on Machine Learning ,Helsinki,Finland, 2008 : 160 ~ 167
Gutmann M , Hyvärinen A . Noise-contrastive estimation: a new estimation principle for unnormalized statistical models . Proceedings on International Conference on Artificial Intelligence and Statistics ,Sardinia,Italy, 2010 : 297 ~ 304
Yang C , Liu Z Y . Comprehend deepwalk as matrix factorization . arXiv Preprint arXiv:1501.00358 , 2015
Goldberg Y , Levy O . Word2vec explained:deriving Mikolov et al.'s negative-sampling word-embedding methodfactorization . arXiv Preprint arXiv:1402.3722 , 2014
Li Y T , Xu L L , Tian F , et al . Word embedding revisited: anew representation learning and explicit matrix factorization perspective . Proceedings of the 24th International Joint Conference on Artificial Intelligence ,Buenos Aires,Argentina, 2015 : 3650 ~ 3656
Yang C , Liu Z Y , Zhao D L , et al . Network representation learning with rich text information . Proceedings of the 24th International Joint Conference on Artificial Intelligence ,Buenos Aires,Argentina, 2015 : 2111 ~ 2117
Yu H F , Jain P , Kar P , et al . Large-scale multi-label learning with missing labels . arXiv Preprint arXiv:1307.5101 , 2013
Tang J , Qu M , Mei Q Z . PTE: predictive text embedding through large-scale heterogeneous text networks . Proceedings of the 21st ACM SIGKDD Conference on knowledge Discovery and Data Mining ,Sydney,Australia, 2015
Ahmed A , Shervashidze N , Narayanamurthy S , et al . Distributed large-scale natural graph factorization . Proceedings of the 22nd International Conference on World Wide Web ,Rio,Brazil, 2013 : 37 ~ 48
0
浏览量
629
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621