Propagate to Discover: Graph-Structured Propagation for Generalized Category Discovery

Generalized Category Discovery (GCD) aims to recognize both known and novel categories from a small labeled set and a large unlabeled pool. Existing methods often rely on sparse supervision to shape the representation space, which can lead to unstable separation between old and new categories, especially in fine-grained recognition scenarios. In this paper, we propose PD-GCD, a graph-structured propagation framework that views sparse labels as semantic seed signals and unlabeled samples as nodes on a data manifold. PD-GCD first learns a GCD-oriented representation space and performs Anchor Mining to identify propagation anchors from the unlabeled pool, balancing semantic coverage with boundary-aware exploration. These anchors are then used by Graph Propagation to spread semantic information over the data graph and produce propagated soft labels. To further align model predictions with the propagated structure, we introduce graph-guided structural distillation and graph consistency regularization, forming a closed-loop process between anchor mining, label propagation, and representation refinement. Extensive experiments on six benchmarks demonstrate that PD-GCD achieves state-of-the-art performance, with absolute All-accuracy gains of 6.7\% across all datasets and 9.4\% on fine-grained datasets.