What Governs Unified Comprehension and Generation on Graphs? A Controlled Study

Vision and language have converged on unified models that both understand and generate, yet graph pretraining usually discards its generative decoder and deploys the encoder alone. We ask what governs a single graph model's ability to support both tasks. A controlled evaluation of a compact graph autoencoder across five domains and eight training conditions separates three effects. First, the generation degradation seen with frozen decoders comes from refitting the latent prior on the fine-tuned encoder. It shrinks as a weight anchor limits encoder drift, and the tested generator families shift the trade-off less than this prior deployment on three of five domains. Second, a null-calibrated learned metric exposes generation discrepancies that descriptor MMD hides and changes which conditions are not significantly dominated. Third, the comprehension gain of retaining the decoder, significant only on Synthetic and CiteSeer, is reproduced by a weight anchor with no generative loss on Synthetic but not on CiteSeer. Only an active generative loss reduces the mismatch between the current encoder and the deployed decoder relative to frozen decoders, on every domain under descriptor MMD and outside QM9 under C2ST, without pinning the encoder to its pretrained weights.