GetCom : An Efficient and Generalizable Framework for Community Detection

Community detection plays a pivotal role in network analysis, with applications in recommendation systems, anomaly detection, and biochemistry. However, traditional methods, while computationally efficient, often fall short in managing the complexities of real-world network structures. In contrast, deep learning approaches enhance accuracy but require substantial computational resources and task-specific architectures. This paper introduce GetCom, a novel three-phase "pre-train, generate, prompt" framework that integrates traditional methods and deep learning techniques. In the pre-training phase, GetCom acquires comprehensive understanding of community structures, which provides a solid foundation for the subsequent phases. During the generation phase, traditional community detection methods are employed to efficiently identify potential communities, which are subsequently refined in the prompt learning phase. This integration offers an efficient, accurate, and generalizable solution for community detection. Experiments on five real-world network datasets demonstrate that GetCom achieves state-of-the-art performance, with strong efficiency and generalization capabilities across diverse datasets and tasks.