Clustering Services Based on Community Detection in Service Networks

Abstract

Service-oriented computing has become a promising way to develop software by composing existing services on the Internet. However, with the increasing number of services on the Internet, how to match requirements and services becomes a difficult problem. Service clustering has been regarded as one of the effective ways to improve service matching. Related work shows that structure-related similarity metrics perform better than semantic-related similarity metrics in clustering services. Therefore, it is of great importance to propose much more useful structure-related similarity metrics to improve the performance of service clustering approaches. However, in the existing work, this kind of work is very rare. In this paper, we propose a SCAS (service clustering approach using structural metrics) to group services into different clusters. SCAS proposes a novel metric A2S (atomic service similarity) to characterize the atomic service similarity as a whole, which is a linear combination of C2S (composite-sharing similarity) and A3S (atomic-service-sharing similarity). Then, SCAS applies a guided community detection algorithm to group atomic services into clusters. Experimental results on a real-world data set show that our SCAS performs better than the existing approaches. Our A2S metric is promising in improving the performance of service clustering approaches.