Decoder-only Clustering in Attributed Graphs
Yik Lun Kei Oscar Hernan Madrid Padilla Rebecca Killick James Wilson Xi Chen Robert Lund
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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.引用本文复制引用
Yik Lun Kei,Oscar Hernan Madrid Padilla,Rebecca Killick,James Wilson,Xi Chen,Robert Lund.Decoder-only Clustering in Attributed Graphs[EB/OL].(2026-05-06)[2026-05-08].https://arxiv.org/abs/2511.04859.学科分类
计算技术、计算机技术
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