可扩展的基于GNN的知识图谱表示学习与高效消息传递
Scalable GNN-based Knowledge Graph Representation Learning with Efficient Message Passing
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中文总结 AI 辅助
本文提出扩展RSPMM以支持更表达力的组合函数，实现知识图谱上GNN消息传递的无损高效计算，显著降低运行时和空间开销，同时保持接近最先进的性能。
中文摘要 AI 辅助
图神经网络（GNNs）在知识图谱（KGs）上的表示学习中表现出色，在链接预测或实体分类等任务上达到了最先进的性能。然而，其固有的用户定义消息传递（MP）算法带来的高计算复杂度，仍阻碍其广泛应用，尤其是在大规模知识图谱上。当前缓解知识图谱上GNN可扩展性瓶颈的努力，如子图采样，通常针对特定任务和模型，且不能可靠地保证无损（如适用）的运行时/空间缩减。为解决此问题，我们扩展了关系稀疏矩阵乘法（RSPMM），该算法最初设计用于无损降低基于组合的消息传递（使用逐点组合函数）的空间复杂度，现支持更具表达力的函数（如2x2块对角矩阵乘法、Givens旋转、循环相关）。我们的方法在运行时和空间上为知识图谱上的现有GNN提供了显著的任务无关缩减，并促进了GNN的高效重新实现，在挑战性知识图谱任务上以极小计算成本保持接近最先进的性能。
英文摘要
Graph neural networks (GNNs) excel at representation learning on Knowledge Graphs (KGs), achieving stateof-the-art performance on tasks like link prediction or entity classification. However, their high computational complexity, inherent to their user-defined message passing (MP) algorithm, still prohibits their widespread adoption, especially for large KGs. Current efforts to mitigate the scalability bottlenecks of GNNs on KGs, such as subgraph sampling, are often task- and model-specific, and do not reliably guarantee lossless (if applicable) runtime/space reductions. To address this, we extend Relational Sparse Matrix Multiplication (RSPMM), originally designed to losslessly lower the space complexity of composition-based MP with pointwise composition functions, to support more expressive functions (e.g., 2x2 block-diagonal matrix multiplication, Givens rotation, circular correlation). Our method delivers significant task-independent reductions in runtime and space for current GNNs on KGs and facilitates efficient re-implementations of GNNs that maintain near state-of-the-art performance on challenging KG tasks, for a fraction of computational costs.
发表机构
- Bosch Center for Artificial Intelligence（博世人工智能中心）
- University of Mannheim（曼海姆大学）
机构由 AI 辅助整理，请以论文原文为准。
