UniCG: A Framework for Unifying Graph Comprehension and Generation

The pursuit of general-purpose intelligence has motivated unified architectures that both comprehend and generate content, with notable progress in language and vision. However, prevailing graph learning paradigms still treat these capabilities separately: comprehension models map graphs to predictions, whereas generative models synthesize graphs without being evaluated for comprehension. Consequently, enabling a single model to comprehend and generate graphs through a shared representation remains a major challenge. In this paper, we propose UniCG, a novel framework for Unifying graph Comprehension and Generation that addresses both the representational incompatibility and optimization interference between these capabilities. Specifically, to establish a common graph interface, UniCG combines lossless graph serialization with residual quantization of node semantics to construct a shared discrete graph vocabulary. To support both capabilities within one model, UniCG formulates generation as next-token prediction and comprehension as a pooled read-out over a single autoregressive backbone. To alleviate the asymmetric interference exposed by joint training, UniCG routes the two objectives through task-decoupled experts while retaining the shared backbone. Extensive experiments across 20 graph domains, four comprehension tasks, and three generation regimes against 13 baselines demonstrate that UniCG jointly supports both capabilities, achieving competitive comprehension performance while generating valid and novel graphs.