A Service Clustering Based on GCN Unsupervised Community Detection

Web service clustering technique can effectively improves the service retrieval efficiency. Service networks offer new possibilities to handle the huge growth of Web services; Community detection is one of the important tasks in Web data mining to efficiently analyze and understand the structural properties and group characteristics of various networks. In view of this, this paper proposes a service clustering method based on unsupervised community detection. First, a structure center update strategy is used to overcome the dependence on the initial structure center; Second, the label propagation model is based on the GCN model as a base module, which can utilize both the network topology and node attributes. In order to improve the model's label propagation capability, the method extends the pseudo-label set as supervisory information to train the model and is used to infer the community labels of the remaining nodes. Finally, experiments conducted on 4 real networks show that the method has better community detection performance.