Online Unified Graph Pruning for Compact Graph Neural Networks

Graph neural networks (GNNs) have achieved strong performance on graph learning tasks, but their deployment is often limited by the computational cost caused by dense graph structures and high-dimensional hidden representations. Existing pruning methods usually focus on either graph sparsification or model compression, and often treat these two pruning dimensions independently. In this paper, we study Online Unified Graph Pruning (OUGP), which jointly learns graph-edge sparsity and parameter sparsity during GNN training. The key idea is to use hidden representations as feedback signals to couple graph pruning and parameter pruning, so that the compact model can preserve task-relevant structural and semantic information. The final model is materialized as a compact GNN with fixed graph and parameter masks. To improve deployment robustness under distribution shift, we further introduce an Energy-Shift Parameter-Mask adaptation module (ESPM), which adjusts parameter selection according to unlabeled target-domain statistics while preserving the compact structure. Experiments compare OUGP+ESPM with dense and existing unified pruning baselines, and evaluate its accuracy, sparsity, efficiency, and robustness under distribution shift. For instance, under 30% graph and 30% model sparsity, OUGP+ESPM improves accuracy by 0.92%$\uparrow$ on Cora, while reducing training FLOPs by 25%$\downarrow$ compared with the dense GCN. The code is anonymously available at [link](https://anonymous.4open.science/r/OUGP-release-E92A/).