Decoder-only Clustering in Attributed Graphs
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866913092642799616 |
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| author | Kei, Yik Lun Padilla, Oscar Hernan Madrid Killick, Rebecca Wilson, James Chen, Xi Lund, Robert |
| author_facet | Kei, Yik Lun Padilla, Oscar Hernan Madrid Killick, Rebecca Wilson, James Chen, Xi Lund, Robert |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04859 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decoder-only Clustering in Attributed Graphs Kei, Yik Lun Padilla, Oscar Hernan Madrid Killick, Rebecca Wilson, James Chen, Xi Lund, Robert Methodology Computation 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. |
| title | Decoder-only Clustering in Attributed Graphs |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2511.04859 |