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Resolution-Aware Structural Density Peak Clustering

NeurIPS 2026 · Graph learning › Graph algorithms and clustering · approach: Neural and local graph clustering

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Proposes a resolution-aware structural variant of density peak clustering that performs density-peak inference on resolution-controlled structural units rather than individual samples.

Problem

Density Peak Clustering is cutoff-sensitive in density estimation and vulnerable to assignment chain errors, giving suboptimal results under heterogeneous densities and complex cluster structures.

Approach

Shifts density peak inference from the sample level to resolution-controlled structural units, so that density becomes more stable and label propagation is less chain-vulnerable.

Experimental setup

Builds on: density peak clustering

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