Decoder-only Clustering in Attributed Graphs

TL;DR

This paper introduces a neural decoder-based clustering method for attributed graphs, integrating structural and attribute data through graph-fused regularization and demonstrating effectiveness on simulations and real data.

Contribution

It presents a novel framework combining neural decoders, priors, and graph-fused regularization for improved nodal clustering in attributed graphs.

Findings

Effective clustering demonstrated on grid graphs.

Method outperforms existing approaches in complex real data settings.

Optimization via ADMM and Langevin dynamics is successful.

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.