Incremental GNN Embedding Computation on Streaming Graphs
Qiange Wang, Haoran Lv, Yanfeng Zhang, Weng-Fai Wong, Bingsheng He

TL;DR
This paper introduces an incremental GNN embedding computation framework for streaming graphs that significantly reduces redundant calculations, improves efficiency, and supports various GNN models through a decoupled, reordering approach and GPU-CPU co-processing.
Contribution
It proposes a novel incremental RTEC framework that decouples GNN operators, reorders computations, and employs GPU-CPU co-processing to enhance efficiency on streaming graphs.
Findings
Reduces computation by 64%-99%.
Achieves 1.7x-145.8x speedups over existing methods.
Supports diverse GNN models with complex message-passing.
Abstract
Graph Neural Network (GNN) on streaming graphs has gained increasing popularity. However, its practical deployment remains challenging, as the inference process relies on Runtime Embedding Computation (RTEC) to capture recent graph changes. This process incurs heavyweight multi-hop graph traversal overhead, which significantly undermines computation efficiency. We observe that the intermediate results for large portions of the graph remain unchanged during graph evolution, and thus redundant computations can be effectively eliminated through carefully designed incremental methods. In this work, we propose an efficient framework for incrementalizing RTEC on streaming graphs.The key idea is to decouple GNN computation into a set of generalized, fine-grained operators and safely reorder them, transforming the expensive full-neighbor GNN computation into a more efficient form over the…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Graph Theory and Algorithms · Big Data and Digital Economy
