New Recipe for Semi-supervised Community Detection: Clique Annealing under Crystallization Kinetics
Keywords: Semi-supervised Community Detection, Clique, Annealing, Crystallization Kinetics
TL;DR: semi-supervised community detection approach premised on crystallization kinetics
Abstract: 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 \ie, 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 diverse community detection datasets demonstrate that CLANN outperforms state-of-the-art methods across multiple real-world datasets, showcasing its exceptional efficacy and efficiency in community detection.
Primary Area: learning on graphs and other geometries & topologies
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Submission Number: 6488
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