基于生成对抗网络的异质信息网络表征学习

鉴于传统的异质信息网络通常存在的高维稀疏性缺点，首先提出将异质信息网络的高维顶点嵌入低维向量空间的无监督学习模型——基于生成对抗网络的异质网络表征学习（heterogeneous network representation learning based on generative adversarialnetwork，HNRL-GAN）模型；然后分析HNRL-GAN模型中的不足之处，进一步提出改进后的基于生成对抗网络的增强版异质网络表征学习（heterogeneous network representationlearning based on generative adversarial network plus plus，HNRL-GAN++）模型；最后分别在DBLP、Yelp、Aminer等数据集中使用HNRL-GAN模型和HNRL-GAN++模型进行节点分类和节点聚类等实验以测试模型的有效性。实验结果表明：1）HNRL-GAN模型和HNRL-GAN++模型都实现了将异质信息网络中的高维稀疏节点表示为低维稠密向量这一目标；2）相较于HNRL-GAN模型，HNRL-GAN++模型在保留高维空间中网络结构信息和语义信息等方面拥有更好的性能。

Heterogeneous Information Network Representation Learning Based on Generative Adversarial Network

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.