Run GGNN propagation

TGN-1 Order: #1 Model Training

Updated 4 months ago

Guidance

For each step: compute messages m(v)_k = linear(h(v)) per edge type. Aggregate incoming messages via elementwise summation. Update node state via GRU: h'(v) = GRU(aggregated_messages, h(v)). Repeat for exactly 8 steps — fewer is insufficient; more does not help.

Details

Order:: #1
Phase:
Created:: May 20, 2026
Last Updated:: May 20, 2026

Workflow

Train GGNN

Configure task-specific graph modifications, batch graphs as disconnected components, run 8-step GGNN propagation, and train with maximum likelihood.

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