A Heterogeneous Graph Attention Network-Based Web Service Link Prediction

With the rise of service computing, the increasing number and diversity of web services make it an intractable task to search for suitable services. Service composition, service selection and recommendation have become the focus of service computing. As the fundamental research of service network, service link prediction is used to explore the composition mode between services, which can facilitate the development of service composition, service selection and recommendation. However, the existing link prediction methods are mainly based on manual modeling and derivation, which cannot make full use of the global structure information and perform poorly in complex networks. The challenging problem in service link prediction is the heterogeneity and sparseness of the service network. Therefore, we propose a novel web service link prediction method based on a heterogeneous graph attention network. By analyzing the interaction between services, five types of neighbors that are associated with service links are chosen, and two levels of attention are applied to learn the importance of neighbors and calculate the embedding of services. In addition, in order to improve accuracy, we design a Service-TextRank algorithm to extract the key information of the service description. Extensive experimental results on real-world data-ProgrammableWeb validate the effectiveness of our approach.