[1] S. Bhagat, G. Cormode, and S. Muthukrishnan, "Node Classification in Social Networks, In: Aggarwal C. (eds)," in
Social Network Data Analytics. Springer, Boston, MA, 2011.
[2] P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, "Collective classification in network data," AI magazine, Vol. 29, No. 3, pp. 93-106, 2008.
|
[3] Gao, Fei, Katarzyna Musial, Colin Cooper, and Sophia Tsoka., "Link prediction methods and their accuracy for different social networks and network metrics," Scientific programming , 2015 .
|
[4] D. L.-N. a. J. Kleinberg, "The link-prediction problem for social networks," Journal of the American society for information science and technology, Vol. 58, No. 7, pp. 1019-1031, 2007.
|
[5] Jamali, Mohsen and Martin Ester, "A matrix factorization technique with trust propagation for recommendation in social networks," in Proceedings of the fourth ACM conference on Recommender systems, 2010.
|
[6] X. Yu, X. Ren, Y. Sun, Q. Gu, B. Sturt, U. Khandelwal, B. Norick, and J. Han, "Personalized entity recommendation: A heterogeneous information network approach," WSDM, pp. 283-292, 2014.
|
[7] M. Belkin and P. Niyogi, "Laplacian eigenmaps and spectral techniques for embedding and clustering," Advances in neural information processing, pp. 585-591, 2002.
|
[8] J. B. Tenenbaum, V. De Silva, and J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, Vol. 290(5500), pp. 2319–2323, 2000.
|
[9] S. T. Roweis and L. K. Saul, "Non-linear dimensionality reduction by locally linear embedding," Science, Vol. 290(5500), pp. 2323, 2326, 2000.
|
[10] T. F. Cox and M. A. Cox, "Multi-dimensional scaling," In: Handbook of data visualization, Springer, pp. 315-347, 2000.
|
[11] S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Q. Yang, and S. Lin, "Graph embedding and extensions: a general framework for dimensionality reduction," IEEE transactions on pattern analysis and machine intelligence, Vol. 29, No. 1, pp. 40–51, 2007.
|
[12] A. Ahmed, N. Shervashidze, S. Narayanamurthy V. Josifovski, and A. J. Smola, "Distributed large-scale natural graph factorization," in Proceedings of the 22nd international conference on World Wide Web, 2013.
|
[13] Ou. M, Cui. P, Pei. J. Zhang. Z, and Zhu. W, "Asymmetric transitivity preserving graph embedding," in In: Proceedings of the 22nd ACM SIGKDD in-ternational conference on Knowledge discovery and data mining, 2016.
|
[14] Henderson, K., Gallagher, B., Li, L., Akoglu, L., Eliassi-Rad, T., Tong, H., and Faloutsos, C., "It’s who you know: graph mining using recursive structural features.," in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining., 2011.
|
[15] T. Mikolov, K. Chen, G.Corrado, and J. Dean, "Efficient estimation of word representations in vector space," in arXiv preprint arXiv:1301.3781, 2013.
|
[16] B. Perozzi, R. Al-Rfou, and S. Skiena, "“Deepwalk: Online learning of social representations," in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, (KDD, 2014), 2014.
|
[17] A. Grover and J. Leskovec, "node2vec: Scalable Feature Learning for Networks," in Proceedings of the 22nd ACM SIGKDD international conference (KDD ’16), San Francisco, 2016.
|
[18] Tang J, Qu M, Wang M, Zhang M, Yan J, and Mei Q, "LINE : Large-scale Information Network Embedding Categories and Subject Descriptors," In: Proceedings of the 24th international conferenceon World Wide Web, pp. 1067–1077, 2015.
|
[19] D. Wang, P. Cui, and W. Zhu, "Structural deep network embedding," in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2016), 2016.
|
[20] David M Blei, Andrew Y Ng, and Michael Jordan, "Latent dirichlet allocation," the Journal of machine Learning research, Vol. 3, pp. 993–1022, 2003.
|
[21] Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Y Chang, "Network representation learning with rich text information," in Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015.
|
[22] Pan, Shirui, Jia Wu, Xingquan Zhu, Chengqi Zhang, and Yang Wang, "Tri-party deep network representation," in Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16), 2016.
|
[23] Keikha, Mohammad Mehdi, Maseud Rahgozar, and Masoud Asadpour, "Community aware random walk for network embedding," Knowledge-based Systems, Vol. 148, pp. 47-54, 2018.
|
[24] Hongyan Xu, Hongtao Liu, Wenjun Wang, Yueheng Sun, NE-FLGC: Network Embedding based on Fusing Local (First-Order) and Global (Second-Order) Network Structure with Node Content, Advances in Knowledge Discovery and Data Mining, 2018.
|
[25] Quoc V Le and Tomas Mikolov, "Distributed representations of sentences and documents," in InternationalConference on Machine Learning, 2014.
|
[26] Chen, Haochen, Bryan Perozzi, Rami Al-Rfou, and Steven Skiena, "A tutorial on network embeddings.," arXiv preprint arXiv :1808.02590, 2018.
|
[27] Abbas Ahmed and Josef Holmberg, "Information extraction from short text messages," in LU-CS-EX 2019-18.
|
[28] Kozma, Robert, Cesare Alippi, Yoonsuck Choe, and Francesco Carlo Morabito, eds., Artificial Intelligence in the Age of Neural networks and Brain computing, Academic Press, 2018..
|
[29] Taheri, Aynaz, Kevin Gimpel, and Tanya Berger-Wolf, "Sequence-to-sequence modeling for graph representation learning," Applied Network Science 4, Vol. 1, No. 68, 2019.
|
[30] Cui, Yiming, Shijin Wang, and Jianfeng Li, "LSTM neural reordering feature for statistical machine translation," arXiv preprint arXiv, 2015.
|
[31] Greff, Klaus, Rupesh K. Srivastava, Jan Koutník, Bas R. Steunebrink, and Jürgen Schmidhuber, "LSTM: A search space odyssey," IEEE transactions on neural networks and learning systems, vol. 28, No. 10, pp. 2222-2232, 2016.
|
[32] Graves, Alex, Navdeep Jaitly, and Abdel-rahman Mohamed, "Hybrid speech recognition with deep bidirectional LSTM," in IEEE workshop on automatic speech recognition and understanding, IEEE, 2013.
|
[33] Han, Song, Junlong Kang, Huizi Mao, Yiming Hu, Xin Li, Yubin Li, Dongliang Xie et al., "Ese: Efficient speech recognition engine with sparse lstm on fpga.," in Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2017.
|
[34] Chen, Minghai, Guiguang Ding, Sicheng Zhao, Hui Chen, Qiang Liu, and Jungong Han, "Reference-based LSTM for image captioning," in Thirty-First AAAI Conference on Artificial Intelligence, 2017.
|
[35] Aneja, Jyoti, Aditya Deshpande, and Alexander G. Schwing, "Convolutional image captioning," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5561-5570, 2018.
|
[36] Staudemeyer, Ralf C. , Eric Rothstein Morris, "Understanding LSTM-a tutorial into Long Short-Term Memory Recurrent Neural Networks.," arXiv preprint arXiv, p. 1909.09586, 2019.
|
[37] Afshine Amidi and Shervine Amidi, "Recurrent Neural Networks cheatsheet," [Online]. Available: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks.
|
[38] Wang, Haohan, and Bhiksha Raj, "On the origin of deep learning," arXiv preprint arXiv:1702.07800, 2017.
|
[39] Mikolov, Tomas, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean, "Distributed representations of words and phrases and their compositionality," In Advances in neural information processing systems, pp. 3111-3119, 2013.
|
[40] S. Cavallari, V. W. Zheng, H. Cai, K. C.-C. Chang, and E.Cambria, "Learning community embedding with community detection and node embedding on graphs," in the 26th ACM International Conference on Information and Knowledge Management, (CIKM ’17), 2017.
|
[41] Jonathan Chang and David M Blei, "Relational topic models for document networks," in International Conference on Artificial Intelligence and Statistics, 2009.
|
[42] A. Reihanian, M. R. Feizi-Derakhshi, and H. S. Aghdasi, "Overlapping community detection in rating-based social networks through analyzing topics, ratings and links," in Pattern Recognition, 2018.
|
[43] Laurens Van Der Maaten and Geoffrey Hinton, "Visualizing data using t-sne," Journal of Machine Learning Research, Vol. 9, pp. 2579–2605, 2008.
|
[44] Günce Keziban Orman, Vincent Labatut, and Hocine Cherifi, "Qualitative Comparison of Community Detection Algorithms," in International conference on digital information and communication technology and its applications.s. Springer, Berlin, 2011.
[45] Batagelj, Vladimir, " Efficient algorithms for citation network analysis," arXiv preprint cs/0309023, 2003.
|
|
[46] Garfield E, ": From Computational Linguistics to Algorithmic Historiography," in at the Symposium in Honor of Casimir Borkowski at the University of Pittsburgh School of Information Sciences,, Pittsburgh , 2001.
|
[47] Waltman, Ludo, and Erjia Yan., "PageRank-related methods for analyzing citation networks," In Measuring scholarly impact, pp. 83-100. Springer, Cham, 2014.
|
[48] Mariani, Manuel Sebastian, Matúš Medo, and François Lafond., "Early identification of important patents: Design and validation of citation network metrics," Technological forecasting and social change , vol. 146, pp. 644-654., 2019.
|
[49] Baggio, Jacopo A., Katrina Brown, and Denis Hellebrandt., "Boundary object or bridging concept? A citation network analysis of resilience.," Ecology and Society , Vol. 20, No. 2, 2015.
|
[50] Zarezade, M., E. Nourani, and Asgarali Bouyer. "Community detection using a new node scoring and synchronous label updating of boundary nodes in social networks." Journal of AI and Data Mining 8, no. 2, pp. 201-212., 2020.