Adversarial network embedding using structural similarity

Abstract

Network embedding which aims to embed a given network into a low-dimensional vector space has been proved effective in various network analysis and mining tasks such as node classification, link prediction and network visualization. The emerging network embedding methods have shifted of emphasis in utilizing mature deep learning models. The neuralnetwork based network embedding has become a mainstream solution because of its high efficiency and capability of preserving the nonlinear characteristics of the network. In this paper, we propose Adversarial Network Embedding using Structural Similarity (ANESS), a novel, versatile, low-complexity GANbased network embedding model which utilizes the inherent vertex-to-vertex structural similarity attribute of the network. ANESS learns robustness and effective vertex embeddings via a adversarial training procedure. Specifically, our method aims to exploit the strengths of generative adversarial networks in generating high-quality samples and utilize the structural similarity identity of vertexes to learn the latent representations of a network. Meanwhile, ANESS can dynamically update the strategy of generating samples during each training iteration. The extensive experiments have been conducted on the several benchmark network datasets, and empirical results demonstrate that ANESS significantly outperforms other state-of-theart network embedding methods.

Keywords

network embedding / structural similarity / generative adversarial network

References

[1] Cui P, Wang X, Pei J, Zhu W W. A survey on network embedding. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(5): 833–852

[2] 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. 2014, 701–710

[3] Tsoumakas G, Katakis I. Multi-label classification: an overview. International Journal of Data Warehousing and Mining, 2007, 3(3): 1–13

[4] Xu XW, Yuruk N, Feng Z D, Schweiger T A. Scan: a structural clustering algorithm for networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2007, 824–833

[5] Maaten L, Hinton G. Visualizing data using t-SNE. Journal of Machine Learning Research, 2008, 9(Nov): 2579–2605

[6] Liben-Nowell D, Kleinberg J. The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 2007, 58(7): 1019–1031

[7] Lee J, Tukhvatov R. Evaluations of similarity measures on vk for link prediction. Data Science and Engineering, 2018, 3(3): 277–289

[8] Tang J, Qu M, Wang MZ, Zhang M, Yan J, Mei Q Z. Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web. 2015, 1067–1077

[9] 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. 2016, 855–864

[10] Wang D X, Cui P, Zhu W W. Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016, 1225–1234

[11] Ban Y F, Pu J H, Chen Y J, Wang Y H. Negan: network embedding based on generative adversarial networks. In: Proceedings of the International Joint Conference on Neural Networks. 2018, 1–8

[12] Wang H W, Wang J, Wang J L, Zhao M, Zhang W N, Zhang F Z, Xie X, Guo M Y. Graphgan: graph representation learning with generative adversarial nets. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2018, 2508–2515

[13] Dai Q Y, Li Q, Tang J, Wang D. Adversarial network embedding. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2018, 2167–2174

[14] Gao H C, Huang H. Self-paced network embedding. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2018, 1406–1415

[15] Sang L, Xu M, Qian S, Wu X D. AAANE: attention-based adversarial autoencoder for multi-scale network embedding. In: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2019, 3–14

[16] Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Proceedings of Advances in Neural Information Processing Systems. 2014, 2672–2680

[17] Cao S S, Lu W, Xu Q K. Grarep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management. 2015, 891–900

[18] Yang C, Sun M S, Liu Z Y, Tu C C. Fast network embedding enhancement via high order proximity approximation. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017, 19–25

[19] Wang X, Cui P, Wang J, Pei J, Zhu W W, Yang S Q. Community preserving network embedding. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2017, 203–209

[20] Wold S, Esbensen K, Geladi P. Principal component analysis. Journal of Chemometrics and Intelligent Laboratory Systems, 1987, 2(1): 37–52

[21] Lathauwer L D, Moor B D, Vandewalle J. A multilinear singular value decomposition. Journal of Matrix Analysis and Applications, 2000, 21(4): 1253–1278

[22] Mikolov T, Sutskever I, Chen K, Corrado G S, Dean J. Distributed representations of words and phrases and their compositionality. In: Proceedings of Advances in Neural Information Processing Systems. 2013, 3111–3119

[23] Lyu T S, Zhang Y, Zhang Y. Enhancing the network embedding quality with structural similarity. In: Proceedings of the 26th ACM International Conference on Information and Knowledge Management. 2017, 147–156

[24] Cao S S, L u W, Xu Q K. Deep neural networks for learning graph representations. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2016, 1145–1152

[25] Kipf T N, Welling M. Variational graph auto-encoders. 2016, arXiv preprint arXiv:1611.07308

[26] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. In: Proceedings of International Conference on Learning Representations. 2017

[27] Velikovi P, Fedus W, Hamilton W L, Liò P, Bengio Y, Hjelm R D. Deep graph infomax. In: Proceedings of International Conference on Learning Representations. 2019

[28] Gao H C, Pei J, Huang H. Progan: network embedding via proximity generative adversarial network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2019, 1308–1316

[29] Sun Y W, Wang S H, Hsieh T, Tang X F, Honavar V G. MEGAN: a generative adversarial network for multi-view network embedding. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2019, 3527–3533

[30] Hu B, Fang Y, Shi C. Adversarial learning on heterogeneous information networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2019, 120–129

[31] Yu W C, Zheng C, Cheng W, Aggarwal C C, Song D J, Zong B, Chen H F, Wang W. Learning deep network representations with adversarially regularized autoencoders. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2018, 2663–2671

[32] Chang L J, Li W, Lin X M, Qin L, Zhang W J. Pscan: fast and exact structural graph clustering. In: Proceedings of the 32nd IEEE International Conference on Data Engineering. 2016, 253–264

[33] Yang C, Liu Z Y, Zhao D L, Sun M S, Chang E Y. Network representation learning with rich text information. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence. 2015, 2111–2117

RIGHTS & PERMISSIONS

Higher Education Press