As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentially growing neighborhoods. While various system-level and algorithmic optimizations have been proposed to accelerate Graph Neural Network (GNN) pipelines, data management and movement remain the primary bottlenecks at scale. In this paper, we explore whether graph sparsification, a well-established technique that reduces edges to create sparser neighborhoods, can serve as a lightweight pre-processing step to address these bottlenecks while preserving model quality. We develop an extensible experimental framework that integrates graph sparsification into GNN training and inference pipelines, and use it to study how four representative sparsification methods interact with four GNN architectures, across six graphs and two learning tasks (node classification and link prediction). Our central finding is counter-intuitive: removing a large fraction of a graph's edges, often the majority, has only a small effect on model quality, and sometimes improves it. For example, the K-Neighbor sparsifier removes 83.3% of the edges of the Products graph while GCN accuracy drops by only 0.2 percentage points, and on the Arxiv link prediction task, sparsification raises GraphSAGE test AUC from 0.86 to 0.95. Because the runtime costs scale with the number of edges, these reductions translate into substantial speedups. Our results show that the pre-processing overhead is quickly amortized, making sparsification practical even for very large graphs.

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