The Strong Lottery Ticket Hypothesis (SLTH) demonstrates the existence of high-performing subnetworks within a randomly initialized model, discoverable through pruning a convolutional neural network (CNN) without any weight training. A recent study, called Untrained GNNs Tickets (UGT), expanded SLTH from CNNs to shallow graph neural networks (GNNs). However, discrepancies persist when comparing baseline models with learned dense weights. Additionally, there remains an unexplored area in applying SLTH to deeper GNNs, which, despite delivering improved accuracy with additional layers, suffer from excessive memory requirements. To address these challenges, this work utilizes Multicoated Supermasks (M-Sup), a scalar pruning mask method, and implements it in GNNs by proposing a strategy for setting its pruning thresholds adaptively. In the context of deep GNNs, this research uncovers the existence of untrained recurrent networks, which exhibit performance on par with their trained feed-forward counterparts. This paper also introduces the Multi-Stage Folding and Unshared Masks methods to expand the search space in terms of both architecture and parameters. Through the evaluation of various datasets, including the Open Graph Benchmark (OGB), this work establishes a triple-win scenario for SLTH-based GNNs: by achieving high sparsity, competitive performance, and high memory efficiency with up to 98.7\% reduction, it demonstrates suitability for energy-efficient graph processing.

翻译：强彩票假设（SLTH）揭示了随机初始化模型中存在高性能子网络，可通过无需权重训练的卷积神经网络（CNN）剪枝发现。近期研究"未训练图神经网络彩票"（UGT）将SLTH从CNN拓展至浅层图神经网络（GNN）。然而，与学习型密集权重基线模型相比仍存在差异。此外，SLTH在深层GNN中的应用尚属空白——这类网络虽通过增加层数提升准确率，却面临内存需求量过大的问题。针对上述挑战，本文采用标量剪枝掩码方法多涂层超掩码（M-Sup），通过提出自适应剪枝阈值设定策略将其应用于GNN。在深层GNN研究中，本文发现未训练循环网络的存在，其性能可与同等训练的前馈网络相媲美。本文还提出多阶段折叠与非共享掩码方法，以扩展架构和参数空间的搜索范围。通过涵盖开放图基准（OGB）在内的多数据集评估，基于SLTH的GNN实现了三重优势：在保持高稀疏性、竞争性性能的同时，内存效率提升高达98.7%，验证了其适用于节能图神经网络处理。