Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2026

Abstract

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 Generative 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 propose 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 principled and practical for semi-supervised community detection.

Keywords

annealing, clique, crystallization kinetics, Semi-supervised community detection

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Knowledge and Data Engineering

Volume

38

Issue

4

First Page

2195

Last Page

2208

ISSN

1041-4347

Identifier

10.1109/TKDE.2026.3659444

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TKDE.2026.3659444

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