References(29)
[1]
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, Collective classification in network data, AI Mag., vol. 29, no. 3, pp. 93-106, 2008.
[2]
I. Herman, G. Melancon, and M. S. Marshall, Graph visualization and navigation in information visualization: A survey, IEEE Trans. Vis. Comput. Graph., vol. 6, no. 1, pp. 24-43, 2000.
[3]
D. Liben-Nowell and J. Kleinberg, The link-prediction problem for social networks, J. Amer. Soc. Inf. Sci. Technol., vol. 58, no. 7, pp. 1019-1031, 2007.
[4]
J. B. Tenenbaum, V. De Silva, and J. C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science, vol. 290, no. 5500, pp. 2319-2323, 2000.
[5]
S. T. Roweis and K. S. Lawrence, Nonlinear dimensionality reduction by locally linear embedding, Science, vol. 290, no. 5500, pp. 2323-2326, 2000.
[6]
M. Belkin and P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, in Proc. 14th Int. Conf. Neural Information Processing Systems: Natural and Synthetic, Vancouver, Canada, 2001, pp. 585-591.
[7]
M. Chen, Q. Yang, and X. O. Tang, Directed graph embedding, in Proc. 20th Int. Joint Conf. Artificial Intelligence, Hyderabad, India, 2007, pp. 2707-2712.
[8]
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, Distributed representations of words and phrases and their compositionality, in Advances in Neural Information Processing Systems 26, Lake Tahoe, NV, USA, 2013, pp. 3111-3119.
[9]
T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space, arXiv preprint arXiv: 1301.3781, 2013.
[10]
T. Mikolov, W. T. Yih, and G. Zweig, Linguistic regularities in continuous space word representations, in Proc. 2013 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Atlanta, GA, USA, 2013, pp. 746-751.
[11]
B. Perozzi, R. Al-Rfou, and S. Skiena, DeepWalk: Online learning of social representations, in Proc. 20th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, New York, NY, USA, 2014, pp. 701-710.
[12]
J. Tang, M. Qu, M. Z. Wang, M. Zhang, J. Yan, and Q. Z. Mei, LINE: Large-scale information network embedding, arXiv preprint arXiv: 1503.03578, 2015.
[13]
A. Grover and J. Leskovec, node2vec: Scalable feature learning for networks, in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 855-864.
[14]
T. Tang, M. Qu, and Q. Z. Mei, PTE: Predictive text embedding through large-scale heterogeneous text networks, in Proc. 21st ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Sydney, Australia, 2015, pp. 1165-1174.
[15]
D. X. Wang, P. Cui, and W. W. Zhu, Structural deep network embedding, in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 1225-1234.
[16]
S. S. Cao, L. Wei, and Q. K. Xu, Deep neural networks for learning graph representations, in Proc. 13th AAAI Conf. Artificial Intelligence, Phoenix, AZ, USA, 2016, pp. 1145-1152.
[17]
C. Cortes and V. Vapnik, Support-vector networks, Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995.
[18]
F. Tian, B. Gao, Q. Cui, E. H. Chen, and T. Y. Liu, Learning deep representations for graph clustering, in Proc. 28th AAAI Conf. Artificial Intelligence, Québec City, Canada, 2014, pp. 1293-1299.
[19]
P. Vincent, H. Larochelle, Y. Bengio, and P. A. Manzagol, Extracting and composing robust features with denoising autoencoders, in Proc. 25th Int. Conf. Machine Learning, Helsinki, Finland, 2008, pp. 1096-1103.
[20]
S. Y. Chang, W. Han, J. L. Tang, G. J. Qi, C. C. Aggarwal, and T. S. Huang, Heterogeneous network embedding via deep architectures, in Proc. 21th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Sydney, Australia, 2015, pp. 119-128.
[21]
L. Page, S. Brin, R. Motwani, and T. Winograd, The PageRank citation ranking: Bringing order to the web. Stanford InfoLab, Stanford University, Stanford, CA, USA, 1999.
[22]
M. E. J. Newman, Finding community structure in networks using the eigenvectors of matrices, Phys. Rev. E, vol. 74, p. 036104, 2006.
[23]
X. Wang, P. Cui, J. Wang, J. Pei, W. W. Zhu, and S. Q. Yang, Community preserving network embedding, in Proc. 31st AAAI Conf. Artificial Intelligence, San Francisco, CA, USA, 2017, pp. 203-209.
[24]
D. P. Kingma and B. Jimmy, Adam: A method for stochastic optimization, arXiv preprint arXiv: 1412.6980, 2014.
[25]
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proc. IEEE, vol. 86, vol. 11, pp. 2278-2324, 1998.
[26]
A. K. McCallum, K. Nigam, J. Rennie, and K. Seymore, Automating the construction of internet portals with machine learning, Information Retrieval, vol. 3, no. 2, pp. 127-163, 2000.
[27]
X. Zhang, W. Z. Chen, Z. M. Xie, and H. F. Yan, Learning transductive network embedding, J. Front. Comput. Sci. Technol., vol. 11, no. 4, pp. 520-527, 2017.
[28]
C. C. Tu, W. C. Zhang, Z. Y. Liu, and M. S. Sun, Max-Margin DeepWalk: Discriminative learning of network representation, in Proc. 25th Int. Joint Conf. Artificial Intelligence, New York, NY, USA, 2016, pp. 3889-3895.
[29]
R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin, LIBLINEAR: A library for large linear classification, J. Mach. Learn. Res., vol. 9, pp. 1871-1874, 2008.