RGSM-GP: Reliability-Guided Semantic Memory for Iterative Graph Purification under Structural Attacks

Iterative graph purification can repair adversarial edge perturbations before classification, but it typically reuses the same node-feature conditioning at every round. As connectivity changes, fixed feature conditioning cannot incorporate the semantic evidence exposed by progressively repaired neighborhoods. This paper introduces Reliability-Guided Semantic Memory for Graph Purification (RGSM-GP), which augments iterative graph purification with a semantic state that evolves across rounds. The method estimates classifier-free node reliability from structural correction residuals, edge-posterior uncertainty, and temporal instability across purification rounds. A budget-normalized soft gate limits updates to unreliable candidates, while reliability-weighted neighborhoods supply the replacement context. The updated memory conditions the next structural step, while raw features remain fixed anchors for training and final prediction. Under a common multi-seed evaluation pipeline on Cora, Citeseer, and PubMed, RGSM-GP attains the highest robust-average point estimate in six of six PRBCD/LRBCD settings among GPR-GAE, AT-GCN, RUNG, and GNNGuard, and also ranks first at the 50% perturbation budget in five settings. In the direct multi-seed comparison with baselines, all six completed robust-average differences favor RGSM-GP, including an improvement of 5.94 percentage points on Cora under PRBCD.