提出FastAlign框架，让网络对齐更快更省资源。
Scalable Optimal Transport Algorithm for Network Alignment
- 将最优传输计算重构为稀疏-密集混合操作，提升效率。
- 在CPU上提速3.89至9.45倍，GPU上提速2.24至32.54倍。
- 适合大规模网络对齐，兼顾精度与计算性能。
网络对齐通过识别不同网络间的节点对应关系，在社交网络分析、欺诈检测和知识图谱融合等数据科学任务中具有基础性作用。然而，现有先进方法常依赖反复构建和更新稠密矩阵，导致可扩展性差。为解决这一问题，我们提出FastAlign——一种基于最优传输的可扩展、稀疏感知网络对齐框架。不引入新对齐模型，而是保留原有最优传输公式，并将其计算重新解释为一系列重复的稀疏-密集混合操作。FastAlign结合稀疏图计算与领域特定的核融合技术，包括自定义的SpMM核。实验表明，FastAlign在对齐质量上与最先进方法相当，同时显著降低端到端运行时间：在CPU上提速3.89至9.45倍，在GPU上提速2.24至32.54倍。
原文摘要 · Abstract (English)
Network alignment identifies node correspondences across different networks and is a fundamental primitive in many data science applications, including social network analysis, fraud detection, and knowledge graph integration. However, state-of-the-art network alignment methods often achieve high accuracy by repeatedly constructing and updating dense matrices, sacrificing scalability in the process. To address this scalability limitation without compromising alignment accuracy, we present FastAlign, a scalable, sparsity-aware framework for optimal transport-based network alignment. Rather than introducing a new alignment model, FastAlign preserves the original OT formulation and reinterprets its computation as a set of recurring mixed sparse-dense operations. FastAlign combines sparsity-aware graph computation with domain-specific kernel fusion, including a custom SpMM kernel. Our results show that FastAlign achieves alignment quality comparable to state-of-the-art OT-based methods while substantially reducing end-to-end runtime up to 3.89x-9.45x on CPU and 2.24x-32.54x on GPU.
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