用图谱理论动态剪枝异步GNN，提升效率不降性能
Spectral Theory for Edge Pruning in Asynchronous Recurrent Graph Neural Networks
- 基于拉普拉斯矩阵特征值虚部设计动态剪枝策略
- 实验证明可减少40%以上冗余边且保持95%以上准确率
- 适合追求高效动态图建模的科研与工程人员
图神经网络（GNN）已成为处理图结构数据的强大工具，广泛应用于社交网络分析和分子生物学等领域。在各类GNN中，异步循环图神经网络（ARGNN）因其能捕捉动态图中的复杂依赖关系而脱颖而出，其特性类似生物体的复杂自适应机制。然而，其高复杂性常导致模型庞大且计算成本高昂。因此，剪枝无用边对提升效率至关重要，同时不显著降低性能。本文提出一种基于图谱理论的动态剪枝方法，利用网络图拉普拉斯矩阵特征值的虚部实现边缘剪枝。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on graph-structured data, finding applications in numerous domains including social network analysis and molecular biology. Within this broad category, Asynchronous Recurrent Graph Neural Networks (ARGNNs) stand out for their ability to capture complex dependencies in dynamic graphs, resembling living organisms' intricate and adaptive nature. However, their complexity often leads to large and computationally expensive models. Therefore, pruning unnecessary edges becomes crucial for enhancing efficiency without significantly compromising performance. This paper presents a dynamic pruning method based on graph spectral theory, leveraging the imaginary component of the eigenvalues of the network graph's Laplacian.
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