How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?

Name: How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?
Uploaded: 2026-10-09T01:58:00.484151+00:00
Duration: 316 s
Description: Probabilistic Graphical Models (PGMs) outperform Graph Neural Networks (GNNs) when input features are low-dimensional or noisy, and they are more robust to increasing graph heterophily. PGMs work well even with sparse node attributes, unlike GNNs which require explicit feature matrices. The findings suggest that linear models can outperform GNNs in link prediction tasks.

Probabilistic Graphical Models (PGMs) outperform Graph Neural Networks (GNNs) when input features are low-dimensional or noisy, and they are more robust to increasing graph heterophily. PGMs work well even with sparse node attributes, unlike GNNs which require explicit feature matrices. The findings suggest that linear models can outperform GNNs in link prediction tasks.

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