Clique Annealing: Semi-Supervised Community Detection Under Crystallization Kinetics

Semi-supervised community detection seeks to find a specified community type when only few communities are labeled. Existing “select-then-refine” pipelines often start from mis-aligned cores and rely on Reinforcement-Learning or Gen-erative Adversarial Network, increasing computational cost and limiting scalability. We address these issues with a unified energy framework under crystallization kinetics that jointly models energy, structure, and growth. Based on this perspective, we pro-pose CLique ANNealing (CLANN), which first employs Nucleus Proposer to select candidate clique as community core under four physics-inspired criteria. A learning-free Transitive Annealer then iteratively merges neighboring cliques and repositions the nucleus, enabling spontaneous, scalable community growth. Evaluated on diverse real-world and synthetic networks, CLANN surpasses state-of-the-art baselines by a wide margin while running faster on large graphs, demonstrating that the energy-driven crystallization kinetics framework is both princi-pled and practical for semi-supervised community detection.