AGREE is an end-to-end framework for heterogeneous attributed graph clustering that uses quaternion representations, multi-level alignment, and shallow architectures to mitigate over-smoothing and over-dominating effects while jointly optimizing for reconstruction and clustering.
A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions,
3 Pith papers cite this work. Polarity classification is still indexing.
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CoOL clusters data by training neural networks via expectation-maximization gradients to assign groups based on agreement.
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Bridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learning
AGREE is an end-to-end framework for heterogeneous attributed graph clustering that uses quaternion representations, multi-level alignment, and shallow architectures to mitigate over-smoothing and over-dominating effects while jointly optimizing for reconstruction and clustering.
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Cohort Organized Learning: Clustering Through Agreement
CoOL clusters data by training neural networks via expectation-maximization gradients to assign groups based on agreement.
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