AI 中文总结
本文提出一种基于随机化算子草图的超快代数哈希方法，直接从图结构生成紧凑二进制节点表示，无需特征或训练，在十个数据集上优于现有无特征基线且生成速度亚秒级，并兼容神经形态计算。
AI 中文摘要
图神经网络通常依赖稠密的浮点节点表示，这会带来显著的内存和计算开销。二进制图哈希通过将节点信息编码为紧凑的比特串提供了一种替代方案。然而，现有方法要么为了计算效率而牺牲全局拓扑信息，要么产生高昂的生成成本。我们提出了一种超快速的、完全代数化的哈希方法，该方法直接从图结构构建二进制节点表示，无需节点特征或基于梯度的训练。我们的方法利用受Nyström方法启发的随机列采样来近似高阶结构转移矩阵，并将其与一种高效的标签安全语义传播机制相结合。所得的连续表示通过逐列阈值化进行离散化，以获得紧凑的二进制码。在十个节点分类数据集上的实验表明，所提出的方法在分类准确率上持续优于现有的无特征二进制基线，同时在许多数据集上实现了亚秒级的代码生成。所得的二进制表示还天然适用于事件驱动计算，使其与神经形态脉冲神经网络和无梯度学习规则兼容。这些结果表明，简单的代数近似可以为离散图表示学习提供一种高效的学习流水线替代方案。
英文摘要
Graph neural networks typically rely on dense, floating-point node representations, which can impose substantial memory and computational costs. Binary graph hashing offers an alternative by encoding node information as compact bit strings. However, existing approaches either sacrifice global topological information for computational efficiency or incur substantial generation costs. We introduce an ultra-fast, entirely algebraic hashing method that constructs binary node representations directly from graph structure, without requiring node features or gradient-based training. Our method approximates a high-order structural transition matrix using randomized column sampling inspired by the Nyström method and combines it with an efficient label-safe semantic propagation mechanism. The resulting continuous representations are discretized through column-wise thresholding to obtain compact binary codes. Experiments on ten node classification datasets show that the proposed method consistently improves classification accuracy over existing feature-free binary baselines while requiring sub-second code generation on many datasets. The resulting binary representations are also naturally suited to event-driven computation, making them compatible with neuromorphic spiking neural networks and gradient-free learning rules. These results demonstrate that simple algebraic approximations can provide an efficient alternative to learned pipelines for discrete graph representation learning.
