基于生成对抗网络的异质信息网络表征学习

doi:10.3969/j.issn.0255-8297.2021.04.002

Abstract

Abstract: In view of the high-dimensional sparsity shortcomings of traditional heterogeneous information networks, we firstly proposed an unsupervised learning model-heterogeneous network representation learning based on generative adversarial network (HNRL-GAN) that embeds the high-dimensional vertices of heterogeneous information networks into low-dimensional vector spaces. Secondly, having analyzed the shortcomings of HNRL-GAN, we proposed an improved model, called as heterogeneous network representation learning based on generative adversarial network plus plus (HNRL-GAN++). Finally, we used HNRL-GAN and HNRL-GAN++ in three data sets, including DBLP, Yelp, and Aminer, to perform node classification and node clustering for testing the effectiveness of the two models. Experimental results show that: 1) Both HNRL-GAN and HNRL-GAN++ achieve the goal of representing high-dimensional sparse nodes in heterogeneous information networks as low-dimensional dense vectors; 2) Compared with HNRL-GAN, HNRL-GAN++ has better performance in retaining network structure information and semantic information in high-dimensional space.

Key words: heterogeneous information network, generative adversarial network, network representation learning

CLC Number:

TP391

LIU Xinghong, WANG Ying, WANG Xin, LAN Shumei. Heterogeneous Information Network Representation Learning Based on Generative Adversarial Network[J]. Journal of Applied Sciences, 2021, 39(4): 532-544.

References

[1] Sun Y, Han J. Mining heterogeneous information networks:a structural analysis approach[J]. ACM SIGKDD Explorations News Letter, 2013, 14(2):20-28.
[2] Sun Y, Norick B, Han J, et al. Integrating meta-path selection with user-guided object clustering in heterogeneous information networks[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY:ACM Press, 2012:1348-1356.
[3] Kong X, Yu P S, Ding Y, et al. Meta path-based collective classification in heterogeneous information networks[C]//Proceedings of the ACM International Conference on Information and Knowledge Management. New York, NY:ACM Press, 2012:1567-1571.
[4] Cao B, Kong X, Yu P S. Collective prediction of multiple types of links in heterogeneous information networks[C]//Proceedings of the IEEE International Conference on Data Mining. Piscataway, NJ:IEEE Press, 2015:50-59.
[5] Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks[J/OL]. arXiv preprint arXiv:1406.2661, 2014. (2014-06-10)[2020-06-10]. https://arxiv.org/abs/1406.2661.
[6] Wang H, Wang J. GraphGAN:graph representation learning with generative adversarial nets[J/OL]. arXiv preprint arXiv:1711.08267, 2017. (2017-11-22)[2020-06-10]. https://arxiv.org/abs/1711.08267.
[7] Dong Y, Chawla N V, Swami A. Metapath2vec:scalable representation learning for heterogeneous networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY:ACM Press, 2017:135-144.
[8] Shang J, Qu M, Liu J, et al. Meta-path guided embedding for similarity search in large-scale heterogeneous information networks[J/OL]. arXiv preprint arXiv:1610.09769, 2016. (2016-10-31)[2020-06-10]. https://arxiv.org/abs/1610.09769.
[9] Xu L, Wei X, Cao J, et al. Embedding of embedding (EOE):joint embedding for coupled heterogeneous networks[C]//Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. New York, NY:ACM Press, 2017:741-749.
[10] Fu T, Lee W C, Lei Z. HIN2Vec:explore meta-paths in heterogeneous information networks for representation learning[C]//Proceedings of the 26th ACM International Conference on Information and Knowledge Management. New York, NY:ACM Press, 2017:1797-1806.
[11] Wang H, Zhang F, Hou M, et al. SHINE:signed heterogeneous information network embedding for sentiment link prediction[J/OL]. arXiv preprint arXiv:1712.00732, 2017. (2017-12-03)[2020-06-10]. https://arxiv.org/abs/1712.00732.
[12] Cai X, Han J, Yang L. Generative adversarial network based heterogeneous bibliographic network representation for personalized citation recommendation[C]//AAAI Conference on Artificial Intelligence. Palo Alto, CA:AAAI Press, 2018:5747-5754.
[13] Dai Q, Li Q, Tang J, et al. Adversarial network embedding[OL]. arXiv preprint arXiv:1711.07838, 2017. (2017-11-21)[2020-06-10]. https://arxiv.org/abs/1711.07838.
[14] Yu W, Zheng C, Cheng W, et al. Learning deep network representations with adversarially regularized autoencoders[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY:ACM Press, 2017:2663-2671.
[15] Pan S, Hu R, Long G, et al. Adversarially regularized graph autoencoder for graph embedding[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. Sacramento, CA:IJCAI Press, 2018:2609-2615.
[16] Hu B, Fang Y, Shi C. Adversarial learning on heterogeneous information networks[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY:ACM Press, 2019:120-129.

Heterogeneous Information Network Representation Learning Based on Generative Adversarial Network

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