AI RESEARCH
Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelines
arXiv CS.LG
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ArXi:2603.06952v1 Announce Type: new 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.