首页|Inductive Lottery Ticket Learning for Graph Neural Networks
Inductive Lottery Ticket Learning for Graph Neural Networks
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维普
Graph neural networks(GNNs)have gained increasing popularity,while usually suffering from unaffordable computations for real-world large-scale applications.Hence,pruning GNNs is of great need but largely unexplored.The re-cent work Unified GNN Sparsification(UGS)studies lottery ticket learning for GNNs,aiming to find a subset of model pa-rameters and graph structures that can best maintain the GNN performance.However,it is tailed for the transductive set-ting,failing to generalize to unseen graphs,which are common in inductive tasks like graph classification.In this work,we propose a simple and effective learning paradigm,Inductive Co-Pruning of GNNs(ICPG),to endow graph lottery tickets with inductive pruning capacity.To prune the input graphs,we design a predictive model to generate importance scores for each edge based on the input.To prune the model parameters,it views the weight's magnitude as their importance scores.Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their impor-tance scores.Although it might be strikingly simple,ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings.On 10 graph-classification and two node-classification benchmarks,ICPG achieves the same performance level with 14.26％-43.12％sparsity for graphs and 48.80％-91.41％spar-sity for the GNN model.
万方数据
维普
