Graph Neural Networks (GNNs) have shown promising performance in various graph learning tasks, but at the cost of resource-intensive computations. The primary overhead of GNN update stems from graph propagation and weight transformation, both involving operations on graph-scale matrices. Previous studies attempt to reduce the computational budget by leveraging graph-level or network-level sparsification techniques, resulting in downsized graph or weights. In this work, we propose Unifews, which unifies the two operations in an entry-wise manner considering individual matrix elements, and conducts joint edge-weight sparsification to enhance learning efficiency. The entry-wise design of Unifews enables adaptive compression across GNN layers with progressively increased sparsity, and is applicable to a variety of architectural designs with on-the-fly operation simplification. Theoretically, we establish a novel framework to characterize sparsified GNN learning in view of a graph optimization process, and prove that Unifews effectively approximates the learning objective with bounded error and reduced computational load. We conduct extensive experiments to evaluate the performance of our method in diverse settings. Unifews is advantageous in jointly removing more than 90% of edges and weight entries with comparable or better accuracy than baseline models. The sparsification offers remarkable efficiency improvements including 10-20x matrix operation reduction and up to 100x acceleration in graph propagation time for the largest graph at the billion-edge scale.

翻译：图神经网络（GNN）在各类图学习任务中展现出卓越性能，但代价是高昂的计算开销。其更新的主要负担源于图传播和权重变换，两者均涉及图级矩阵运算。现有研究尝试通过图级或网络级稀疏化技术来降低计算预算，从而压缩图结构或权重规模。本文提出Unifews框架，以逐元素方式统一上述两类操作（即考虑单个矩阵元素），并通过联合边-权重稀疏化提升学习效率。该框架的逐元素设计支持跨网络层的自适应压缩（逐层渐进增强稀疏性），并能通过动态运算简化适配多种架构。在理论上，我们建立了一个新颖的框架，从图优化过程视角刻画稀疏化GNN学习，并证明Unifews能以有限误差和降低的计算负载有效逼近学习目标。我们通过广泛实验评估了该方法在多场景下的性能：Unifews能在保持与基线模型相当或更优精度的前提下，联合移除超过90%的边和权重条目。这种稀疏化带来了显著的效率提升，包括矩阵运算量减少10-20倍，以及对于十亿级边规模的最大图，其图传播时间加速高达100倍。