Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelines

TL;DR

An extensible experimental framework is developed that enables systematic evaluation of how different sparsification methods affect the performance and accuracy of GNN models, and the first comprehensive study of GNN training and inference on sparsified graphs is conducted.

Abstract

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 accuracy on node classification tasks. We develop an extensible experimental framework that enables systematic evaluation of how different sparsification methods affect the performance and accuracy of GNN models. We conduct the first comprehensive study of GNN training and inference on sparsified graphs, revealing several key findings. First, sparsification often preserves or even improves predictive performance. As an example, random sparsification raises the accuracy of the GAT model by 6.8% on the PubMed graph. Second, benefits increase with scale, substantially accelerating both training and inference. Our results show that the K-Neighbor sparsifier improves model serving performance on the Products graph by 11.7x with only a 0.7% accuracy drop. Importantly, we find that the computational overhead of sparsification is quickly amortized, making it practical for very large graphs.

Figures (8)

• Figure 1: Overview of our experimental framework
• Figure 2: Comparison of test accuracy and convergence speedup ratio of models trained on sparsified graphs over models trained on the original graphs. The star markers indicate configurations that achieved the best performance for each dataset-model pair. Light gray bars in the speedup plot indicate cases where the sparsification causes accuracy degradation above $1\%$.
• Figure 3: Accuracy--time trade-off across graph sparsification methods. Each point shows a dataset-model-method result, with test accuracy on the y-axis and time to converge on the x-axis (log scale in seconds).
• Figure 4: Time-to-target accuracy speedup across graph sparsification methods. Each panel corresponds to a dataset, with grouped bars showing the speedup factor for each sparsification method per model. The dashed line marks speedup $= 1\times$ (no speedup). An "X" marker indicates that the method could not reach the target accuracy defined by the original graph.
• Figure 5: Time to accuracy on the Products dataset. Each panel shows one model, with per-epoch test accuracy plotted against cumulative training time. The dashed horizontal line marks the target accuracy (best test accuracy on the original graph). Sparsification methods that cross the target line sooner achieve faster time to accuracy.