graph_posterior_network

probly.method.graph_posterior_network(input_encoder: Predictor[In, Out], latent_dim: int, num_classes: int, *, encoder_dim: int | None = None, num_flows: int = 6, class_counts: list | None = None, evidence_scale: GraphPosteriorEvidenceScale = 'latent-new', propagation_steps: int = 10, teleport_probability: float = 0.1, add_self_loops: bool = True) → GraphPosteriorNetworkPredictor[In, Out][source]

Create a Graph Posterior Network predictor.

Parameters:

  • input_encoder – Predictor applied to data.x before density modeling.

  • latent_dim – Latent dimensionality used by the normalizing flow.

  • num_classes – Number of output classes.

  • encoder_dim – Output dimensionality of input_encoder. Inferred when omitted.

  • num_flows – Number of radial flow layers per class.

  • class_counts – Optional class-count prior. If omitted, counts are inferred from data.train_mask.

  • evidence_scale – Additive log-scale for feature evidence.

  • propagation_steps – Number of APPNP propagation steps.

  • teleport_probability – APPNP teleport probability.

  • add_self_loops – Whether APPNP should add self-loops.

Returns:

A graph posterior network predictor returning Dirichlet alphas.