Dynamic Community Detection for Web Service Discovery: An Incremental Approach with Temporal Weighting

Web service discovery in dynamic and large-scale environments poses significant challenges due to the continuous evolution of services and the diversity of user needs. This paper proposes an incremental, community-driven approach for context-aware service discovery. By modeling user-service interactions and semantic descriptions (SAWSDL) within a heterogeneous graph, the system detects and maintains functional communities through incremental label propagation. Each service and user is dynamically labeled to reflect evolving usage patterns and semantic domains. When a service request is issued, discovery is performed within the most relevant community, and candidate services are ranked using both semantic similarity and a novel Community Impact Index (IIC), which reflects the expertise of users within the target community.