[1] Peng C , Xiao W , Jian P , et al. A survey on network
embedding[J]. IEEE Transactions on Knowledge and Data
Engineering, 2018, 31(5): 833-852
[2] Benson A R , Gleich D F , Leskovec J. Higher-order
organization of complex networks[J]. Science, 2016,
353(6295): 163-166.
[3] Cai H, Zheng V W, Chang K C C. A comprehensive survey
of graph embedding: problems, techniques, and
applications[J]. IEEE Transactions on Knowledge andData Engineering, 2018, 30(9): 1616-1637.
[4] Tu C, Yang C, Liu Z, et al. Network representation
learning: an overview[J]. SCIENCE China: Information,
2017, 47(8): 980-996.
涂存超, 杨 成, 刘知远, 等. 网络表示学习综述[J].中
国科学: 信息科学, 2017, 47(8) : 980-996.
[5] Chen W, Zhang Y, Li X. Network representation
learning[J]. Big Data, 2015,1(3): 8-22.
陈维政, 张岩, 李晓明. 网络表示学习[J]. 大数据, 2015,
1(3): 8-22.
[6] Bhagat S, Cormode G, Muthukrishnan S. Node
classification in social networks[M]. Boston: Springer,
2011: 115-148.
[7] Lü L, Zhou T. Link prediction in complex networks: A
survey[J]. Physica A: Statistical Mechanics and Its
Applications, 2011, 390(6): 1150-1170.
[8] Fortunato S. Community detection in graphs[J]. Physics
Reports, 2010, 486(3-5): 75-174.
[9] Maaten L, Hinton G. Visualizing data using t-SNE[J].
Journal of Machine Learning Research, 2008, 9(Nov):
2579-2605.
[10] Chen J, Wu Y, Fan L, et al. N2VSCDNNR: a local
recommender system based on node2vec and rich
information network[J]. IEEE Transactions on
Computational Social Systems, 2019, 6(3): 456-466.
[11] Roweis S T, Saul L K. Nonlinear dimensionality reduction
by locally linear embedding[J]. Science, 2000, 290(5500):
2323-2326.
[12] Belkin M, Niyogi P. Laplacian eigenmaps and spectral
techniques for embedding and clustering[C]//Proceedings
of Advances in Neural Information Processing Systems.
Cambridge: MIT Press, 2001: 585-591.
[13] Cao S, Lu W, Xu Q. Grarep: Learning graph
representations with global structural information[C]
//Proceedings of the 24th ACM International on
Conference on Information and Knowledge Management.
New York: ACM, 2015: 891-900.
[14] 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. New York: ACM, 2016:
1105-1114.
[15] Yang C, Sun M, Liu Z, et al. Fast network embedding
enhancement via high order proximity approximation[C]//
International Joint Conference on Artificial Intelligence.
Palo Alto, California: AAAI Press, 2017: 3894-3900.
[16] 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, USA:
ACM Press, 2014: 701-710.
[17] Grover A, Leskovec J. node2vec: Scalable feature learning
for networks[C]//Proceedings of the 22nd ACM SIGKDD
International Conference on Knowledge Discovery and
Data Mining. New York, USA: ACM Press, 2016:
855-864.
[18] Tang J, Qu M, Wang M, et al. Line: Large-scale
information network embedding[C]//Proceedings of The
24th International Conference on World Wide Web. New
York, USA: ACM, 2015: 1067-1077.
[19] Wang D, Cui P, Zhu W. Structural deep network
embedding[C]//Proceedings of the 22nd ACM SIGKDD
International Conference on Knowledge Discovery and
Data Mining. New York: ACM, 2016: 1225-1234.
[20] Cao S, Lu W, Xu Q. Deep neural networks for learning
graph representations[C]//Thirtieth AAAI Conference on
Artificial Intelligence. Palo Alto, California: AAAI Press,
2016: 1145-1152.
[21] Girvan M, Newman M E J . Community structure in social
and biological networks[J]. PNAS, 2001, 99: 7821-7826.
[22] Cherifi H, Palla G, Szymanski B, et al. On community
structure in complex networks: challenges and
opportunities[J]. Applied Network, 2019.
[23] Newman M E J. Finding community structure in networks
using the eigenvectors of matrices[J]. Physical Review E,
2006, 74(3): 036104. [24] Tu K, Cui P, Wang X, Wang F, Zhu W. Structural deep
embedding for hyper-networks[C]//Thirty-Second AAAI
Conference on Artificial Intelligence. 2018: 426-433.
[25] Wang F, Li T, Wang X, et al. Community discovery using
nonnegative matrix factorization[J]. Data Mining and
Knowledge Discovery, 2011, 22(3): 493-521.
[26] Li Y, Sha C, Huang X, et al. Community detection in
attributed graphs: an embedding approach[C]//
Thirty-Second AAAI Conference on Artificial Intelligence.
New Orleans, USA: AAAI, 2018.
[27] Levy O, Goldberg Y. Neural word embedding as implicit
matrix factorization[C]//Advances in neural information
processing systems. Cambridge: MIT Press, 2014:
2177-2185.
[28] Lee D D, Seung H S. Algorithms for non-negative matrix
factorization[C]//Advances in Neural Information
Processing Systems. Cambridge: MIT Press, 2000:
535-541.
[29] Zachary W. An information flow model for conflict and
fission in small groups[J]. Journal of Anthropological
Research, 1977, 33(4): 452-473.
[30] Craven M, DiPasquo D, Freitag D, et al. Learning to
Extract Symbolic Knowledge from the World Wide
Web[C]//Fifteenth AAAI Conference on Artificial
Intelligence. USA: AAAI, 1998: 1134-1142.
|