Cauchy Graph Convolutional Networks

Taurai Muvunza

doi:10.1016/j.ijar.2025.109517

Cauchy Graph Convolutional Networks

2025-06-19

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DOI：10.1016/j.ijar.2025.109517

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Abstract

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• We introduce Cauchy Graphical Models (CGM) that can be represented as directed acyclic graphs (DAGs) to model impulsive noise in random variables. • We propose Minimum Dispersion Criterion (MDC), a score-based DAG selection method for optimal CGM. • We present Cauchy GCN which leverages CGM-learned graphs to boost GCN classification performance. • We conduct an extensive experimental campaign to validate the efficacy of our approach.

Keywords:

Cauchy distribution

Probabilistic graphical models

Bayesian networks

Heavy-tailed distributions

Graph neural networks

Cauchy Graph Convolutional Networks

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