GNN Inference

GNN inference focuses on efficiently executing trained Graph Neural Networks (GNNs), aiming to overcome computational bottlenecks and improve scalability for real-world applications. Current research emphasizes optimizing inference speed and energy efficiency through techniques like personalized scoping to adapt to graph heterophily, gradient-based approximations for node influence analysis, and hardware acceleration using FPGAs and specialized architectures tailored to exploit graph sparsity. These advancements are crucial for deploying GNNs in resource-constrained environments and handling massive graphs, impacting fields like social network analysis, fraud detection, and drug discovery.