What survives the mask?

Hide one row. Then hide its neighbors.

First hide B. A, C and D still have their original rows. Now hide B, C and D together. B remains connected to all three neighbors, but only A still provides its original row directly. C and D provide mask-token inputs at the encoder's first layer.

The distinction is between a neighbor existing and that neighbor's original features being visible. With multiple message-passing layers, context can travel farther: C connects to F, and D to E. Repeated aggregation expands the paths through which information can influence B. It does not make missing features recoverable by definition.

CL-012 · The comparison counts visible original feature rows. It is not a measured masking-difficulty curve.

A mask ratio leaves out its location

Our masks select one, two, or three of six nodes: approximately 16.7%, 33.3%, and 50%. These are teaching states, not a recommended schedule. Two masks of the same size can preserve different neighborhoods and different signals.

Holding a prediction fixed while changing the scored mask asks: which errors are averaged? Retraining under different masking policies asks: what representations does that learning process produce? The second needs controlled runs, a downstream task, and uncertainty estimates. The first can be answered exactly in a browser.

Define the prediction-time information boundary before pretraining. If an evaluation claims new-graph generalization, using that test graph during pretraining changes the claim, even without labels. A same-graph transductive protocol can be valid; it needs to be named.

CL-010 · Author inductive entry point; evaluation advice here is a qualified inference

Using edges is not reconstructing edges

Our edges determine message-passing neighborhoods. The target remains a feature row such as $(1,2)$. We do not predict whether AB or BC exists. Edge reconstruction needs its own target, output, and objective.

The original GraphMAE paper and reviewed core graphmae/models/edcoder.py path focus on features. There is an optional edge-masking loss branch in the repository's separate chemistry pretraining script. That detail is a reason to name the exact path and configuration, rather than infer behavior from a project name.

CL-001 · GraphMAE, original objective

CL-016 · Separate chemistry script, optional mask_edge branch

We now have the target and scoring set. The remaining choice is what it means for a reconstructed row to match.