MSCCL: A Framework for Enhancing Mashup Service Clustering With Contrastive Learning

Obtaining high-quality service function vectors and aggregating neighborhood features in service association graph are prevalent methods for Mashup service clustering. However, existing methods often focus on enhancing the service functional feature extraction while overlooking distinctions among different services when creating service function vectors. Additionally, neighborhood feature aggregation is typically considered within a single association graph, lacking contrast optimization of different association features. To address these challenges, we propose a novel framework, MSCCL (Mashup Service Clustering with Contrastive Learning). MSCCL consists of two core components: a service function vector generation module and a neighborhood feature aggregation module. Contrastive learning is employed to enhance vector quality and optimize feature aggregation in both modules. We present a service clustering method within MSSCL that combines techniques from BERT (Bidirectional Encoder Representations from Transformers) and GAT (Graph Attention Networks). Compared to state-of-the-art methods, this approach reduces DBI by 2.03% to 12.58%, while enhancing SC, NMI, and Purity by 2.24% to 15.47%, 3.34% to 11.39%, and 2.58% to 13.65%, respectively. Furthermore, the experiments demonstrate that the popular models for service function vector generation and neighborhood feature aggregation can all be integrated into MSSCL. After being integrated into MSSCL, the clustering performance of these models was significantly improved, highlighting the effectiveness and generalizability of MSSCL.