Decoder-only Clustering in Attributed Graphs
Methodology
2026-05-07 v3 Computation
Abstract
This manuscript studies nodal clustering in graphs having multivariate attributes at each node. The framework includes node-specific priors for low-dimensional representations, coupled with a neural decoder that bridges observed attributes with latent variables. Structural and attribute information are incorporated through a graph-fused LASSO regularization on the prior means, promoting nodal clustering. The optimization problem is solved via alternating direction method of multipliers, with Langevin dynamics for posterior inference. Simulation studies on grid graphs, and applications to real data with complex settings, demonstrate the effectiveness of the proposed clustering method.
Cite
@article{arxiv.2511.04859,
title = {Decoder-only Clustering in Attributed Graphs},
author = {Yik Lun Kei and Oscar Hernan Madrid Padilla and Rebecca Killick and James Wilson and Xi Chen and Robert Lund},
journal= {arXiv preprint arXiv:2511.04859},
year = {2026}
}