Beyond this graph

A reusable encoder is a starting point. Transfer is an evaluation claim.

What leaves pretraining?

After pretraining, extract representations with the encoder. The reviewed implementation's embed(g,x) calls the encoder on the supplied graph and features, without the corruption-and-decoder path. A linear probe freezes these representations and trains a small linear predictor. Fine-tuning updates some or all encoder weights for the downstream target.

A probe asks what the representation already makes accessible to a simple predictor. Fine-tuning also asks how it adapts with supervision. Neither question is answered by our lab's reconstruction score.

CL-008 · Author inductive linear evaluation and pretraining entry point

CL-008 · Author embed method, encoder output only

Can it handle a different topology?

A message-passing encoder applies shared local rules, so it can be evaluated on compatible graphs with different numbers of nodes or different adjacency patterns. That is a statement about computation. Accuracy depends on the learned rule, features, graph construction, and training/test distributions.

For a new grid topology, compatible tensor dimensions are not enough. Changed connectivity, feature definitions, operating regimes, or measurement availability may change useful relationships. Our example establishes none of those real-system relationships; the original paper's public benchmarks do not certify a particular grid application.

A defensible evaluation plan makes these decisions before inspecting final test results:

Name the transfer unit. Hold out whole topologies for unseen-topology claims, or declare a same-graph protocol. For temporal transfer, state the cutoff and correlated-sample grouping.
Define the information boundary. Fit preprocessing on allowed training data. State whether test features or edges enter pretraining; unlabeled access is still access.
Compare matched baselines. Use the same downstream labels, splits and tuning budget for random initialization, a supervised baseline, and pretraining. Separate frozen-probe and fine-tuned results.
Report the relevant outcome. Choose metrics and failure slices in advance; include multiple seeds and uncertainty. Report topology-level variation separately from reconstruction diagnostics.

CL-010 · Methodological recommendations inferred from the mechanism and inductive/transductive distinction. This plan has not been executed.

Keep the method names precise

Generic masked autoencoding is a broader pattern. Vision MAE, for example, encodes visible image patches with an asymmetric design; its exact token routing should not be substituted for GraphMAE's graph path.

Original GraphMAE is the feature mechanism traced here. GraphMAE2 adds multi-view random re-masking and latent representation prediction. Those additions differ from original GraphMAE's same-node re-mask.

“GraphMAE+Edge” does not identify a method by itself. A user-specific variant needs its own code, objective, protocol and evidence. Neither the name nor the optional chemistry branch establishes what that variant implements or how it performs.

CL-015 · He et al., Masked Autoencoders, §3

CL-009 · Hou et al., GraphMAE2, §2.2 and Figure 2

CL-011 · User-specific variant remains unverified. No private research results are inferred.

Try it on a document graph

A new graph has text boxes as nodes and reading-order links as edges. Each node stores box area and ink mass in fixed units. Your prescribed reconstruction doubles both entries at every selected node. You get zero SCE. A downstream task needs absolute box area.

What has the reconstruction score established—and what has it missed? How would you evaluate a claim about unseen document layouts?

Work through the transfer answer

Positive doubling preserves every vector direction, so zero SCE establishes directional agreement for nonzero rows. It does not establish correct area or ink mass. Their ratio is preserved; their magnitudes are not. Check the downstream target directly and include per-feature or norm error as a diagnostic.

For unseen-layout transfer, group documents or templates into disjoint train, validation and test sets before fitting. State whether test pages participate in pretraining. A same-page label split and a held-out-template evaluation answer different questions. Compare frozen and fine-tuned encoders against matched baselines, then report measured results and uncertainty. Nothing in our six-node arithmetic supplies those results.

Return to B: we withheld its row, retained its target, routed context through a graph encoder and re-masked decoder, and scored selected reconstruction directions. Every later transfer claim must add evidence beyond that computation.