一种基于GAN的异构信息网络表示学习方法

doi:10.3969/j.issn.1006-2475.2020.05.015

计算机与现代化 ›› 2020, Vol. 0 ›› Issue (05): 89-.doi: 10.3969/j.issn.1006-2475.2020.05.015

一种基于GAN的异构信息网络表示学习方法

(1.贵州大学计算机科学与技术学院，贵州贵阳550025;2.贵州省智能医学影像分析与精准诊断重点实验室，贵州贵阳550025)

收稿日期:2020-01-12 出版日期:2020-05-20 发布日期:2020-05-21
作者简介:周丽(1994-)，女，贵州贵阳人，硕士研究生，研究方向：网络表示学习，E-mail: gzuzhouli@163.com; 申国伟(1986-)，男，湖南邵东人，副教授，博士，研究方向：网络空间安全，大数据，E-mail: gwshen@gzu.edu.cn; 赵文波(1994-)，男，贵州遵义人，本科生，研究方向：自然语言处理，E-mail: zerow_zwb@163.com; 通信作者：周雪梅(1977-)，女，贵州贵阳人，讲师，硕士，研究方向：网络安全，入侵检测技术，E-mail: sherrymn@163.com。
基金资助:
国家自然科学基金资助项目(61802081); 贵州省科技计划项目(［2018］3001)

A Heterogeneous Information Network Represention Learning Method Based on GAN

(1. College of Computer Science and Technology, Guizhou University, Guiyang 550025, China;
2. Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, Guiyang 550025, China)

Received:2020-01-12 Online:2020-05-20 Published:2020-05-21

摘要/Abstract

摘要： 异构信息网络中包含丰富的结构和语义信息，通过网络表示学习保留异构信息网络的结构和语义信息是当前研究的热点。传统的异构信息网络表示学习方法局限于利用元路径的形式保留异构信息网络中的语义信息，缺乏考虑网络中所有节点的分布情况，保留的信息不够充分。因此，本文提出一种基于生成式对抗网络（Generative Adversarial Networks, GAN）的异构信息网络表示学习方法（HINGAN），其能更好地保留网络中的结构信息和语义信息。HINGAN中通过生成模型和判别模型的对抗学习，提高表示学习的鲁棒性。基于2个真实数据集的实验结果表明，本文提出的模型与传统的异构信息网络方法相比，在节点分类和链接预测任务中的结果都有明显提升。

关键词: 网络表示学习, 异构信息网络, 生成式对抗网络

Abstract: Heterogeneous information networks contain rich structural and semantic information. It is a hot topic of current research to retain the structural and semantic information of heterogeneous information networks through network representation learning. Traditional heterogeneous information network representation learning methods are limited to preserving semantic information in heterogeneous information networks in the form of meta-paths, which is lack of considering the distribution of all nodes in the network, and the retained information is insufficient. Therefore, this paper proposes a heterogeneous information network representation learning method (HINGAN) based on a generative adversarial networks (GAN), which can better retain the information in the network and improve the robustness of representation learning. First of all, the GAN generation model generates fake data that retains network information, then samples the real data from the Gaussian distribution, and finally sends the real and fake data into the GAN discriminant model at the same time. Through the game of the generated model and the discriminant model, the node representation with more information is obtained. The experimental results based on two real data sets show that the proposed model is better than the traditional heterogeneous information network method in node classification and link prediction tasks.

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

中图分类号:

TP391

周丽1,2，申国伟1,2，赵文波1,2，周雪梅1,2. 一种基于GAN的异构信息网络表示学习方法[J]. 计算机与现代化, 2020, 0(05): 89-.

ZHOU Li1,2, SHEN Guo-wei1,2, ZHAO Wen-bo1,2, ZHOU Xue-mei1,2. A Heterogeneous Information Network Represention Learning Method Based on GAN[J]. Computer and Modernization, 2020, 0(05): 89-.

参考文献

［1］ CAI H Y, ZHENG V W, CHANG K C C. A comprehensive survey of graph embedding: Problems, techniques, and applications［J］. IEEE Transactions on Knowledge and Data Engineering, 2018,30(9):1616-1637.
［2］ ZHANG D K, YIN J, ZHU X Q, et al. Network representation learning: A survey［J］. arXiv preprint arXiv:1801.05852, 2018.
［3］涂存超,杨成,刘知远,等. 网络表示学习综述［J］. 中国科学:信息科学, 2017,47(8):980-996.
［4］ PEROZZI B, AL-RFOU R, SKIENA S. Deepwalk: Online learning of social representations［C］// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014:701-710.
［5］ TANG J, QU M, WANG M Z, et al. LINE: Large-scale information network embedding［C］// Proceedings of the 24th International Conference on World Wide Web. 2015:1067-1077.
［6］〖JP+1〗GROVER A, LESKOVEC J. Node2vec: Scalable feature learning for networks［C］// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016:855-864.
［7］ TU C C, ZHANG W C, LIU Z Y, et al. Max-margin deepwalk: Discriminative learning of network representation［C］// Proceedings of the 25th International Joint Conference on Artificial Intelligence. 2016:3889-3895.
［8］ JACOB Y, DENOYER L, GALLINARI P. Learning latent representations of nodes for classifying in heterogeneous social networks［C］// Proceedings of the 7th ACM International Conference on Web Search and Data Mining. 2014:373-382.
［9］ TANG J, QU M, MEI Q Z. PTE: Predictive text embedding through large-scale heterogeneous text networks［C］// Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015:1165-1174.
［10］SHANG J B, QU M, LIU J L, et al. Meta-path guided embedding for similarity search in large-scale heterogeneous information networks［J］. arXiv preprint arXiv:1610.09769, 2016.
［11］DONG Y X, 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. 2017:135-144.
［12］FU T Y, LEE W C, LEI Z. Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning［C］// Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 2017:1797-1806.
［13］LU Y F, SHI C, HU L M, et al. Relation structure-aware heterogeneous information network embedding［J］. arXiv preprint arXiv:1905.08027, 2019.
［14］GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets［C］// Proceedings of the 27th International Conference on Neural Information Processing Systems. 2014,2:2672-2680.
［15］YU L T, ZHANG W N, WANG J, et al. SeqGAN: Sequence generative adversarial nets with policy gradient［C］// Proceedings of the 31st AAAI Conference on Artificial Intelligence. 2017:2852-2858.
［16］WANG J, YU L T, ZHANG W N, et al. IRGAN: A minimax game for unifying generative and discriminative information retrieval models［C］// Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2017:515-524.
［17］PAN S R, HU R Q, LONG G D, et al. Adversarially regularized graph autoencoder for graph embedding［C］// Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2018:2609-2615.
［18］WANG H W, WANG J, WANG J L, et al. GraphGAN: Graph representation learning with generative adversarial nets［C］// Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2018:2508-2515.
［19］YU W C, ZHENG C, CHENG W, et al. Learning deep network representations with adversarially regularized autoencoders［C］// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2018:2663-2671.
［20］DAI Q Y, LI Q, TANG J, et al. Adversarial network embedding［C］// Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2018:2167-2174.
［21］DING M, TANG J, ZHANG J. Semi-supervised learning on graphs with generative adversarial nets［C］// Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018:913-922.
［22］HU B B, FANG Y, SHI C. Adversarial learning on heterogeneous information networks［C］// Proceedings of the 25th ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining. 2019:120-129.

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一种基于GAN的异构信息网络表示学习方法

A Heterogeneous Information Network Represention Learning Method Based on GAN

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