A Services Classification Method Based on Heterogeneous Information Networks and Generative Adversarial Networks

Abstract

With the rapid development of service computing and software technologies, it is necessary to correctly and efficiently classify web services to promote their discovery and application. The existing service classification methods based on heterogeneous information networks (HIN) achieve better classification performance. However, such methods use negative sampling to randomly select nodes and do not learn the underlying distribution to obtain a robust representation of the nodes. This paper proposes a web services classification method based on HIN and generative adversarial networks (GAN) named SC-GAN. The authors first construct a HIN using the structural relationships between web services and their attribute information. After obtaining the feature embedding of the services based on meta-path random walks, a relationship-aware GAN model is input for adversarial training to obtain high-quality negative samples for optimizing the embedding. Experimental results on real datasets show that SC-GAN improves classification accuracy significantly over state-of-the-art methods.