Semi-supervised community detection methods are widely used for identifying specific communities due to the label scarcity. Existing semi-supervised community detection methods typically involve two learning stages learning in both initial identification and subsequent adjustment, which often starts from an unreasonable community core candidate. Moreover, these methods encounter scalability issues because they depend on reinforcement learning and generative adversarial networks, leading to higher computational costs and restricting the selection of candidates. To address these limitations, we draw a parallel between crystallization kinetics and community detection to integrate the spontaneity of the annealing process into community detection. Specifically, we liken community detection to identifying a crystal subgrain (core) that expands into a complete grain (community) through a process similar to annealing. Based on this finding, we propose CLique ANNealing (CLANN), which applies kinetics concepts to community detection by integrating these principles into the optimization process to strengthen the consistency of the community core. Subsequently, a learning-free Transitive Annealer was employed to refine the first-stage candidates by merging neighboring cliques and repositioning the community core, enabling a spontaneous growth process that enhances scalability. Extensive experiments on \textbf{43} different network settings demonstrate that CLANN outperforms state-of-the-art methods across multiple real-world datasets, showcasing its exceptional efficacy and efficiency in community detection.

本研究解决了现有半监督社群检测方法在社群核心候选选择不合理和可扩展性差的问题。通过将结晶动力学与社群检测相结合，提出了CLique ANNealing（CLANN）方法，该方法利用动力学原理优化社群核心的一致性，并实现邻近团体的合并与重新定位，从而提升了算法的可扩展性。实验结果表明，CLANN在多种实际数据集上超越了现有的先进方法，展现出卓越的效率和效果。

新型半监督社群检测方法：在结晶动力学下的Clique Annealing