网络表示学习的研究与发展

doi:10.11959/j.issn.2096-109x.2019019

Abstract

Abstract:

Network representation learning is a bridge between network raw data and network application tasks which aims to map nodes in the network to vectors in the low-dimensional space.These vectors can be used as input to the machine learning model for social network application tasks such as node classification,community discovery,and link prediction.The traditional network representation learning methods are based on homogeneous information network.In the real world,the network is often heterogeneous with multiple types of nodes and edges.Moreover,from the perspective of time,the network is constantly changing.Therefore,the research method of network representation learning is continuously optimized with the complexity of network data.Different kinds of network representation learning methods based on different networks were introduced and the application scenarios of network representation learning were expounded.

Key words: large-scale information network, network representation learning, network embedding, deep learning

CLC Number:

TP391.4

Ying YIN,Lixin JI,Ruiyang HUANG,Lixin DU. Research and development of network representation learning[J]. Chinese Journal of Network and Information Security, 2019, 5(2): 77-87.

Figures/Tables 8

References 31

[1]	SUN Y , HAN J . Mining heterogeneous information networks:a structural analysis approach[J]. ACM Sigkdd Explorations Newsletter, 2013,14(2): 20-28.
[2]	ROWEIS S T , SAUL L K . Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000,290(5500): 2323-2326.
[3]	SAUL L K , ROWEIS S T . An introduction to locally linear embedding[J]. Journal of Machine Learning Research, 2008.
[4]	BELKIN M , NIYOGI P . Laplacian eigenmaps and spectral techniques for embedding and clustering[J]. Advances in Neural Information Processing Systems, 2002,14(6): 585-591.
[5]	HE X , NIYOGI P . Locality preserving projections[C]// Advances in Neural Information Processing Systems. 2004: 153-160.
[6]	AHMED A , SHERVASHIDZE N , NARAYANAMURTHY S ,et al. Distributed large-scale natural graph factorization[C]// International Conference on World Wide Web. ACM, 2013: 37-48.
[7]	CAO S , LU W , XU Q . GraRep:learning graph representations with global structural information[C]// ACM International on Conference on Information and Knowledge Management. 2015: 891-900.
[8]	CHENG W , GREAVES C , WARREN M . From n-gram to skipgram to concgram[J]. International Journal of Corpus Linguistics, 2006,11(4): 411-433.
[9]	OU M , CUI P , PEI J ,et al. Asymmetric transitivity preserving graph embedding[C]// The 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 2016: 1105-1114.
[10]	KATZ L . A new status index derived from sociometric analysis[J]. Psychometrika, 1953,18(1): 39-43.
[11]	MIKOLOV T , CHEN K , CORRADO G ,et al. Efficient estimation of word representations in vector space[J]. Computer Science, 2013.
[12]	PEROZZI B,AL-RFOU R , SKIENA S . DeepWalk:online learning of social representations[C]// ACM SIGKDD International Conference on Knowledge Discovery ＆ Data Mining. 2014: 701-710.
[13]	GROVER A , LESKOVEC J . node2vec:scalable feature learning for networks[C]// The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016: 855-864.
[14]	TANG J , QU M , WANG M ,et al. LINE:large-scale information network embedding[C]// International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2015: 1067-1077.
[15]	WANG D , CUI P , ZHU W . structural deep network embedding[C]// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016: 1225-1234.
[16]	HAMILTON W , YING Z , LESKOVEC J . Inductive representation learning on large graphs[C]// Advances in Neural Information Processing Systems. 2017: 1024-1034.
[17]	WANG H , WANG J , WANG J ,et al. GraphGAN:graph representation learning with generative adversarial nets[C]// Thirty-Second AAAI Conference on Artificial Intelligence. 2018.
[18]	TU C , ZHANG W , LIU Z ,et al. Max-margin DeepWalk:discriminative learning of network representation[C]// International Joint Conference on Artificial Intelligence. AAAI Press, 2016.3889:3895.
[19]	YANG C , ZHAO D , ZHAO D ,et al. Network representation learning with rich text information[C]// International Conference on Artificial Intelligence. AAAI Press, 2015: 2111-2117.
[20]	SUN X , GUO J , DING X ,et al. A general framework for content-enhanced network representation learning[J]. arXiv preprint arXiv:1610.02906, 2016.
[21]	PAN S , WU J , ZHU X ,et al. Tri-party deep network representation[C]// International Joint Conference on Artificial Intelligence. AAAI Press, 2016: 1895-1901.
[22]	孙艺洲, 韩家炜 . 异构信息网络挖掘原理与方法[M]. 北京: 机械工业出版社, 2016,2.
	SUN Y Z , HAN J W . Principles and methods of heterogeneous information network mining[M]. Beijing: Machinery Industry PressPress, 2016.
[23]	DONG Y , CHAWLA N V , SWAMI A ,et al. metapath2vec:scalable representation learning for heterogeneous networks[C]// ACM Sigkdd International Conference on Knowledge Discovery ＆ Data Mining. 2017: 135-144.
[24]	SUN G , ZHANG X . Graph embedding with rich information through heterogeneous network[J]. arXiv preprint arXiv:1710.06879, 2017.
[25]	TANG J , QU M , MEI Q . Pte:Predictive text embedding through large-scale heterogeneous text networks[C]// The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015: 1165-1174.
[26]	SHI C , HU B , ZHAO X ,et al. Heterogeneous information network embedding for recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2019,31(2): 357-370.
[27]	SUN Y , HAN J , YAN X ,et al. Pathsim:meta path-based top-k similarity search in heterogeneous information networks[J]. Proceedings of the VLDB Endowment, 2011,4(11): 992-1003.
[28]	SUN Y , NORICK B , HAN J ,et al. PathSelClus:integrating meta-path selection with user-guided object clustering in heterogeneous information networks[J]. ACM Transactions on Knowledge Discovery from Data, 2013,7(3): 1-23.
[29]	LUO C , GUAN R , WANG Z ,et al. Hetpathmine:a novel transductive classification algorithm on heterogeneous information networks[C]// European Conference on Information Retrieval. 2014: 210-221.
[30]	KONG X , YU P S , DING Y ,et al. Meta path-based collective classification in heterogeneous information networks[J]. 2012: 1567-1571.
[31]	FU T Y , LEE W C , LEI Z . HIN2Vec:explore meta-paths in heterogeneous information networks for representation learning[C]// ACM, 2017: 1797-1806.

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	算法	GreRep	HOPE	DeepWalk	Node2vec	LINE	SDNE	GraphGAN
	有向		9		9	9	9
	无向	9	9	9	9	9	9	9
网络属性
	有权	9			9	9		9
	标签
	BlogCatalog	9		9	9	9	9	9
	Flickr			9		9	9
	YouTube			9		9	9
	Wikipedia				9	9		9
实验数据集	PPI				9
	DBLP	9
	20-newsgroup	9					9
	Cora		9
	SN-Twitter		9

算法		MMDW	TADW	CENE	TriDNR
	有向	9	9		9
网络属性	有权
	文本		9	9	9
	标签	9			9
	DBLP			9	9
数据集	CiteSeer	9	9		9
	Cora	9	9
	Wiki	9	9

Research and development of network representation learning

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