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:
Received:
2020-01-12
Online:
2020-05-20
Published:
2020-05-21
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。
中图分类号:
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, doi: 10.3969/j.issn.1006-2475.2020.05.015.
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, doi: 10.3969/j.issn.1006-2475.2020.05.015.
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