Hide a row. Keep its target.

One row, two paths

Let $M$ be the selected nodes. In the clean example, $M=\{B\}$. The encoder receives a corrupted matrix $\widetilde X$, where B's row is replaced by a learned input mask token $m_{\rm in}\in\mathbb{R}^{2}$. The scoring path retains the original $x_B=(1,2)$.

The token is a trainable vector, not the target and not a command to delete B. In the reviewed author code it starts at zero, then can be updated during learning. B remains a node with its edges.

CL-003 · Author implementation, encoding_mask_noise and enc_mask_token

Why mask again?

The encoder computes $H=f_\theta(G,\widetilde X)$. Each row $h_i$ is now a learned representation rather than a raw feature vector. A learned, bias-free projection maps the encoder output into the decoder's input space. In the graph-decoder path, the implementation overwrites the projected rows at the same nodes in M with zero.

Call that re-masked matrix $\widetilde H$. The graph decoder predicts $\widehat X=g_\phi(G,\widetilde H)$, producing a feature row for each node. At B, it has a zero input row and can aggregate neighboring representations. This removes the direct route from B's own encoder row into B's decoder input.

That routing choice does not prove all information about B has vanished elsewhere. Message passing mixes information across connected nodes. Re-masking specifies which local decoder input is removed; it is not an information-theoretic guarantee against shortcuts.

CL-004 · MLP/linear alternatives do not follow this re-masking branch. This guide teaches the graph decoder path.

CL-004 · Author implementation, projection, re-mask and decoder, lines 246–260

The decoder emits more than the loss scores

All six reconstructed rows may exist, but this objective scores only the selected rows. With $M=\{B\}$, an error at A does not enter this step's loss. With $M=\{B,D\}$, the objective averages B's and D's terms. “Masked-only” describes which rows are scored, not which intermediate activations or weights can receive gradients.

The encoder, input token, projection and decoder participate in learning. Our interactive lab will do none of those updates: it evaluates the same loss selection on explicitly prescribed reconstructions.

CL-005 · Author implementation, recon[mask_nodes] and x[mask_nodes]