GHSCN: A Graph Neural Network-Based API Popularity Prediction Method in Service Ecosystem

Abstract

With the rapid development of technologies in the field of service computing, and increasing of complex business requirements, more and more large-scale service ecosystem emerges. Thus, many researches of service ecosystem focus on issues related to optimization such as service recommendation and load balancing, so the API popularity prediction problem studied in this paper, which is basis for this service ecosystem optimization, becomes a research hotspot in this field. However, many existing researches are predicting the popularity of APIs based on API functions, QoS, history usage patterns and social relationships, which are difficult to obtain and cannot reflect the overall structure of the underlying service ecosystem. Therefore, we propose an innovative API popularity prediction method in service ecosystem based on Graph Neural Network (GNN). Concretely, a Global-Service Ecosystem Network (GSEN) model is proposed firstly, for modeling a given service ecosystem to a network that can depict the complex structure of service ecosystem and the functions, QoS, history usage patterns and social relationships of APIs. Then, a Graph Heterogeneous Spatiotemporal Convolutional Network (GHSCN) model is proposed to predict the popularity of APIs based on GSEN, and for getting better prediction accuracy, four different Heterogeneous Spatiotemporal Convolutional Kernels are proposed to extract the features of different elements which have different mechanisms to affect the popularity of target API. Finally, extensive experiments based on the data crawled from ProgrammableWeb.com show that our method achieves a superior performance in API popularity prediction, and the importance of the introduction of our model to service ecosystems.