References

[1] Perozzi B, Al-Rfou R, Skiena S. Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, 2014. 701--710. Google Scholar http://scholar.google.com/scholar_lookup?title=Perozzi B, Al-Rfou R, Skiena S. Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, 2014. 701--710&

[2] Battista G D, Eades P, Tamassia R. Algorithms for drawing graphs: an annotated bibliography. Comp Geometry, 1994, 4: 235-282 CrossRef Google Scholar http://scholar.google.com/scholar_lookup?title=Algorithms for drawing graphs: an annotated bibliography&author=Battista G D&author=Eades P&author=Tamassia R&publication_year=1994&journal=Comp Geometry&volume=4&pages=235-282

[3] Fruchterman T M J, Reingold E M. Graph drawing by force-directed placement. Softw-Pract Exper, 1991, 21: 1129-1164 CrossRef Google Scholar http://scholar.google.com/scholar_lookup?title=Graph drawing by force-directed placement&author=Fruchterman T M J&author=Reingold E M&publication_year=1991&journal=Softw-Pract Exper&volume=21&pages=1129-1164

[4] Kamada T, Kawai S. An algorithm for drawing general undirected graphs. Inf Processing Lett, 1989, 31: 7-15 CrossRef Google Scholar http://scholar.google.com/scholar_lookup?title=An algorithm for drawing general undirected graphs&author=Kamada T&author=Kawai S&publication_year=1989&journal=Inf Processing Lett&volume=31&pages=7-15

[5] Kobourov S G. Spring embedders and force directed graph drawing algorithms,. arXiv Google Scholar http://scholar.google.com/scholar_lookup?title=Kobourov S G. Spring embedders and force directed graph drawing algorithms,&

[6] Roweis S T. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 2000, 290: 2323-2326 CrossRef PubMed ADS Google Scholar http://scholar.google.com/scholar_lookup?title=Nonlinear Dimensionality Reduction by Locally Linear Embedding&author=Roweis S T&publication_year=2000&journal=Science&volume=290&pages=2323-2326

[7] Saul L K, Roweis S T. An introduction to locally linear embedding. 2000. urlhttp://www.cs.toronto.edu/roweis/lle/publications.html. Google Scholar http://scholar.google.com/scholar_lookup?title=Saul L K, Roweis S T. An introduction to locally linear embedding. 2000. urlhttp://www.cs.toronto.edu/roweis/lle/publications.html&

[8] Belkin M, Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, Vancouver, 2001. 585--591. Google Scholar http://scholar.google.com/scholar_lookup?title=Belkin M, Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, Vancouver, 2001. 585--591&

[9] Tang L, Liu H. Leveraging social media networks for classification. Data Min Knowl Disc, 2011, 23: 447-478 CrossRef Google Scholar http://scholar.google.com/scholar_lookup?title=Leveraging social media networks for classification&author=Tang L&author=Liu H&publication_year=2011&journal=Data Min Knowl Disc&volume=23&pages=447-478

[10] Chen M, Yang Q, Tang X. Directed graph embedding. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, Hyderabad, 2007. 2707--2712. Google Scholar http://scholar.google.com/scholar_lookup?title=Chen M, Yang Q, Tang X. Directed graph embedding. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, Hyderabad, 2007. 2707--2712&

[11] Tang L, Liu H. Relational learning via latent social dimensions. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, 2009. 817--826. Google Scholar http://scholar.google.com/scholar_lookup?title=Tang L, Liu H. Relational learning via latent social dimensions. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, 2009. 817--826&

[12] Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality. In: Proceedings of Advances in Neural Information Processing Systems, Lake Tahoe, 2013. 3111--3119. Google Scholar http://scholar.google.com/scholar_lookup?title=Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality. In: Proceedings of Advances in Neural Information Processing Systems, Lake Tahoe, 2013. 3111--3119&

[13] Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space,. arXiv Google Scholar http://scholar.google.com/scholar_lookup?title=Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space,&

[14] Mikolov T, Karafiát M, Burget L, et al. Recurrent neural network based language model. In: Proceedings of International Speech Communication Association, Makuhari, 2010. 1045--1048. Google Scholar http://scholar.google.com/scholar_lookup?title=Mikolov T, Karafiát M, Burget L, et al. Recurrent neural network based language model. In: Proceedings of International Speech Communication Association, Makuhari, 2010. 1045--1048&

[15] Tang J, Qu M, Wang M, et al. Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, Florence, 2015. 1067--1077. Google Scholar http://scholar.google.com/scholar_lookup?title=Tang J, Qu M, Wang M, et al. Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, Florence, 2015. 1067--1077&

[16] Yang C, Liu Z, Zhao D, et al. Network representation learning with rich text information. In: Proceedings of the 24th International Conference on Artificial Intelligence, Buenos Aires, 2015. 2111--2117. Google Scholar http://scholar.google.com/scholar_lookup?title=Yang C, Liu Z, Zhao D, et al. Network representation learning with rich text information. In: Proceedings of the 24th International Conference on Artificial Intelligence, Buenos Aires, 2015. 2111--2117&

[17] Cao S, Lu W, Xu Q. Grarep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, Melbourne, 2015. 891--900. Google Scholar http://scholar.google.com/scholar_lookup?title=Cao S, Lu W, Xu Q. Grarep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, Melbourne, 2015. 891--900&

[18] Yang C, Sun M S, Liu Z Y, et al. Fast network embedding enhancement via high order proximity approximation. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, 2017. Google Scholar http://scholar.google.com/scholar_lookup?title=Yang C, Sun M S, Liu Z Y, et al. Fast network embedding enhancement via high order proximity approximation. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, 2017&

[19] Wang D, Cui P, Zhu W. Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 2016. 1225--1234. Google Scholar http://scholar.google.com/scholar_lookup?title=Wang D, Cui P, Zhu W. Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 2016. 1225--1234&

[20] Yang J, Leskovec J. Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining, Rome, 2013. 587--596. Google Scholar http://scholar.google.com/scholar_lookup?title=Yang J, Leskovec J. Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining, Rome, 2013. 587--596&

[21] Ou M, Cui P, Pei J, et al. Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 2016. 1105--1114. Google Scholar http://scholar.google.com/scholar_lookup?title=Ou M, Cui P, Pei J, et al. Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 2016. 1105--1114&

[22] Tu C, Wang H, Zeng X, et al. Community-enhanced network representation learning for network analysis,. arXiv Google Scholar http://scholar.google.com/scholar_lookup?title=Tu C, Wang H, Zeng X, et al. Community-enhanced network representation learning for network analysis,&

[23] Griffiths T L, Steyvers M. Finding scientific topics. Proc Natl Acad Sci USA, 2004, 101: 5228-5235 CrossRef PubMed ADS Google Scholar http://scholar.google.com/scholar_lookup?title=Finding scientific topics&author=Griffiths T L&author=Steyvers M&publication_year=2004&journal=Proc Natl Acad Sci USA&volume=101&pages=5228-5235

[24] Tu C C, Liu H, Liu Z Y, et al. CANE: context-aware network embedding for relation modeling. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouve, 2017. 1722--1731. Google Scholar http://scholar.google.com/scholar_lookup?title=Tu C C, Liu H, Liu Z Y, et al. CANE: context-aware network embedding for relation modeling. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouve, 2017. 1722--1731&

[25] Tu C C, Zhang W C, Liu Z Y, et al. Max-Margin DeepWalk: discriminative learning of network representation. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), New York, 2016. Google Scholar http://scholar.google.com/scholar_lookup?title=Tu C C, Zhang W C, Liu Z Y, et al. Max-Margin DeepWalk: discriminative learning of network representation. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), New York, 2016&

[26] Li J Z, Zhu J, Zhang B. Discriminative deep random walk for network classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, 2016. 1004--1013. Google Scholar http://scholar.google.com/scholar_lookup?title=Li J Z, Zhu J, Zhang B. Discriminative deep random walk for network classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, 2016. 1004--1013&

[27] Grover A, Leskovec J. Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 2016. 855--864. Google Scholar http://scholar.google.com/scholar_lookup?title=Grover A, Leskovec J. Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 2016. 855--864&

[28] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations, Toulon, 2017. Google Scholar http://scholar.google.com/scholar_lookup?title=Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations, Toulon, 2017&

[29] Yang Z, Cohen W, Salakhutdinov R. Revisiting semi-supervised learning with graph embeddings. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning, New York, 2016. Google Scholar http://scholar.google.com/scholar_lookup?title=Yang Z, Cohen W, Salakhutdinov R. Revisiting semi-supervised learning with graph embeddings. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning, New York, 2016&

[30] Tu C C, Zhang Z Y, Liu Z Y, et al. TransNet: translation-based network representation learning for social relation extraction. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, 2017. Google Scholar http://scholar.google.com/scholar_lookup?title=Tu C C, Zhang Z Y, Liu Z Y, et al. TransNet: translation-based network representation learning for social relation extraction. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, 2017&