网络表示学习

doi:10.11959/j.issn.2096-0271.2015025

Abstract

Abstract:

Along with the constant growth of massive online social networks such as Facebook,Twitter,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.

Key words: network, representation learning, deep learning

CLC Number:

TP181

Weizheng Chen, Yan Zhang, Xiaoming Li. Network Representation Learning[J]. Big Data Research, 2015, 1(3): 8-22.

Figures/Tables 13

References 46

[1]	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.
[2]	Roweis S T , Saul L K . Nonlinear dimensionality reduction by locally linear embedding. Science, 2000,290(5): 2323～2326
[3]	yv?rinen A , Oja E . Independent component analysis: algorithms and applications. Neural Networks, 2000,13(4～5): 411～430
[4]	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.
[5]	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
[6]	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
[7]	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
[9]	Le T , Lauw W . Probabilistic latent document network embedding. Proceedings of 2014 IEEE International Conference on Data Mining (ICDM),Shenzhen,China, 2014:270～279
[10]	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
[11]	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
[12]	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
[13]	Newman M . Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 2006,103(23): 8577～8582
[14]	Zhou D Y , Huang J Y , Sch?lkopf 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
[15]	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
[16]	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
[17]	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.
[18]	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.
[19]	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
[20]	Iwata T , Saito K , Ueda N , et al. Parametric embedding for class visualization. Neural Computation, 2007,19(9): 2536～2556
[21]	Gopalan P , Blei D . Efficient discovery of overlapping communities in massive networks. Proceedings of the National Academy of Sciences, 2013,110(36): 14534～14539
[22]	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.
[23]	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.
[24]	Kobourov S . Spring embedders and force directed graph drawing algorithms. arXiv Preprint 2012,arXiv:1201.3011,2012
[25]	Fruchterman T , Reingold E . Graph drawing by force-directed placement. Software-Practice & Experience, 1991,21(11): 1129～1164
[26]	Kamada T , Kawai S . An algorithm for drawing general undirected graphs. Information Processing Letters, 1989,31(1): 7～15
[27]	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
[22]	Ellson J , Gansner E , Koutsofios L , et al. Graphviz-open source graph drawing tools. Graph Drawing.Berlin Heidelberg:Springer, 2002
[29]	Bengio Y , Goodfellow I , Courville A . Deep Learning. 2015
[30]	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
[31]	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
[32]	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
[33]	Mikolov T , Chen K , Corrado G , et al. Efficient estimation of word representations in vector space. arXiv Preprint arXiv:1301.3781 2013
[34]	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
[35]	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
[36]	Bengio Y , Ducharme R , Vincent P , et al. A neural probabilistic language model. Journal of Machine Learning Research 2003(3): 1137～1155
[37]	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
[38]	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
[39]	Gutmann M , Hyv?rinen 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
[40]	Yang C , Liu Z Y . Comprehend deepwalk as matrix factorization. arXiv Preprint arXiv:1501.00358, 2015
[41]	Goldberg Y , Levy O . Word2vec explained:deriving Mikolov et al.'s negative-sampling word-embedding methodfactorization. arXiv Preprint arXiv:1402.3722, 2014
[42]	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
[43]	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
[44]	Yu H F , Jain P , Kar P , et al. Large-scale multi-label learning with missing labels. arXiv Preprint arXiv:1307.5101, 2013
[45]	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
[46]	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

Metrics

Recommended 0

No Suggested Reading articles found!

网络	节点	标签
DBLP^[15]	论文	会议
Wikipedia^[43]	维基实体	分类
Flickr^[30]	用户	兴趣组
WebKB^[6]	网页	大学

[1]	Yimin DENG, Xulong ZHANG, Shijing SI, Jianzong WANG, Jing XIAO. Human avatars synthesis technologies: a survey [J]. Big Data Research, 2023, 9(3): 114-139.
[2]	Yayun HE, Junqing PENG, Jianzong WANG, Jing XIAO. Rhythm dancer: 3D dance generation by keymotion transition graph and pose-interpolation network [J]. Big Data Research, 2023, 9(1): 23-37.
[3]	Zihang WANG, Xiangqun YU, Hongbiao SI, Simin FU, Xulong ZHANG, Shaoliang PENG. Design of the layered model of the metaverse based on computing network [J]. Big Data Research, 2023, 9(1): 51-62.
[4]	Yumeng CUI, Jingya WANG, Shangyi YAN, Zhizhong TAO. Automatic key information extraction of police records based on deep learning [J]. Big Data Research, 2022, 8(6): 127-142.
[5]	Jun DAI. Emerging scientific topic prediction based on Poincare graph embedding [J]. Big Data Research, 2022, 8(6): 94-104.
[6]	Xinhui LI, Qing SHEN, Xiongtao ZHANG. Classification algorithm for imbalance data of ECG based on PSOFS and TSK fuzzy system [J]. Big Data Research, 2022, 8(5): 139-152.
[7]	Zhitao ZHU, Shijing SI, Jianzong WANG, Jing XIAO. Survey on federated recommendation systems [J]. Big Data Research, 2022, 8(4): 105-132.
[8]	Jie WANG, Songyan ZHANG, Jiye LIANG. A semi-supervised deep learning algorithm combining consistency regularization and manifold regularization [J]. Big Data Research, 2022, 8(3): 103-114.
[9]	Kangting XU, Wei Song. A Chinese text sentiment analysis method combining language knowledge and deep learning [J]. Big Data Research, 2022, 8(3): 115-127.
[10]	Zhitao ZHAO, Lijun ZHAO, Zheng ZHANG, Ping TANG. Integration of remote sensing intelligent processing algorithm using container cloud technology [J]. Big Data Research, 2022, 8(2): 58-74.
[11]	Shaolin AO, Yongbin QIN, Ruizhang HUANG, Yanping CHEN, Lijuan LIU, Qinghua ZHENG, Changheng CHEN, Shaofen CHENG. Research on auxiliary division method based on convolutional neural network [J]. Big Data Research, 2022, 8(2): 145-157.
[12]	Hui HUANG, Yongbin QIN, Yanping CHEN, Ruizhang HUANG. Legal element extraction method based on BERT reading comprehension framework [J]. Big Data Research, 2021, 7(6): 19-29.
[13]	Qian SUN, Yongbin QIN, Ruizhang HUANG, Lijuan LIU, Yanping CHEN. Charge prediction method combined with case elements sequence [J]. Big Data Research, 2021, 7(6): 30-40.
[14]	Baojiang XIN, Dewen LI, Lanlan WANG. Value mining and application of big data in enterprise power credit investigation [J]. Big Data Research, 2021, 7(6): 138-146.
[15]	Jiantong HUO, Limin XIAO, Zhisheng HUO, Yaowen XU. Research and implementation of edge cache system in global virtual data space across WAN [J]. Big Data Research, 2021, 7(5): 57-81.

Network Representation Learning

RichHTML

PDF下载

Knowledge

Cited

Abstract

Cite this article

share this article

Figures/Tables 13

References 46

Related Articles 15

Metrics

Recommended 0